{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sn\n",
    "\n",
    "%matplotlib inline\n",
    " \n",
    "\n",
    "params={'legend.fontsize':'x-large',\n",
    "\n",
    "        'figure.figsize':(10,20),\n",
    "\n",
    "        'axes.labelsize':'x-large',\n",
    "\n",
    "        'axes.titlesize':'x-large',\n",
    "\n",
    "        'xtick.labelsize':'x-large',\n",
    "\n",
    "        'ytick.labelsize':'x-large'\n",
    "\n",
    "        }\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "pd.options.display.max_colwidth=600\n",
    "\n",
    "from IPython.display import display,HTML\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
      "0        1  2011-01-01       1   0     1        0        6           0   \n",
      "1        2  2011-01-02       1   0     1        0        0           0   \n",
      "2        3  2011-01-03       1   0     1        0        1           1   \n",
      "3        4  2011-01-04       1   0     1        0        2           1   \n",
      "4        5  2011-01-05       1   0     1        0        3           1   \n",
      "\n",
      "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
      "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
      "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
      "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
      "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
      "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
      "\n",
      "    cnt  \n",
      "0   985  \n",
      "1   801  \n",
      "2  1349  \n",
      "3  1562  \n",
      "4  1600  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "train=pd.read_csv('D:/ju/day.csv')\n",
    "\n",
    "print(train.head())\n",
    "\n",
    "print(train.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          instant      season          yr        mnth     holiday     weekday  \\\n",
      "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
      "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
      "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
      "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
      "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
      "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
      "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
      "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
      "\n",
      "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
      "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
      "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
      "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
      "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
      "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
      "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
      "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
      "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
      "\n",
      "            casual   registered          cnt  \n",
      "count   731.000000   731.000000   731.000000  \n",
      "mean    848.176471  3656.172367  4504.348837  \n",
      "std     686.622488  1560.256377  1937.211452  \n",
      "min       2.000000    20.000000    22.000000  \n",
      "25%     315.500000  2497.000000  3152.000000  \n",
      "50%     713.000000  3662.000000  4548.000000  \n",
      "75%    1096.000000  4776.500000  5956.000000  \n",
      "max    3410.000000  6946.000000  8714.000000  \n"
     ]
    }
   ],
   "source": [
    "print(train.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值范围和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值范围和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值范围和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值范围和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "category_features=['season','mnth','weathersit','weekday']\n",
    "for col in category_features:\n",
    "    print('\\n%s属性的不同取值范围和出现的次数'%col)\n",
    "    print(train[col].value_counts())    \n",
    "    #原来是Int型数据变为object型    \n",
    "    train[col]=train[col].astype('object')\n",
    "X_train_cat = train[category_features]\n",
    "X_train_cat = pd.get_dummies(X_train_cat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       temp     atemp       hum  windspeed\n",
      "0  0.355170  0.373517  0.828620   0.284606\n",
      "1  0.379232  0.360541  0.715771   0.466215\n",
      "2  0.171000  0.144830  0.449638   0.465740\n",
      "3  0.175530  0.174649  0.607131   0.284297\n",
      "4  0.209120  0.197158  0.449313   0.339143\n"
     ]
    }
   ],
   "source": [
    " #数值型变量预处理，\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mn_X = MinMaxScaler()\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "temp = mn_X.fit_transform(train[numerical_features])\n",
    "X_train_num = pd.DataFrame(data=temp, columns=numerical_features, index =train.index)\n",
    "print(X_train_num.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 35 columns):\n",
      "instant         731 non-null int64\n",
      "season_1        731 non-null uint8\n",
      "season_2        731 non-null uint8\n",
      "season_3        731 non-null uint8\n",
      "season_4        731 non-null uint8\n",
      "mnth_1          731 non-null uint8\n",
      "mnth_2          731 non-null uint8\n",
      "mnth_3          731 non-null uint8\n",
      "mnth_4          731 non-null uint8\n",
      "mnth_5          731 non-null uint8\n",
      "mnth_6          731 non-null uint8\n",
      "mnth_7          731 non-null uint8\n",
      "mnth_8          731 non-null uint8\n",
      "mnth_9          731 non-null uint8\n",
      "mnth_10         731 non-null uint8\n",
      "mnth_11         731 non-null uint8\n",
      "mnth_12         731 non-null uint8\n",
      "weathersit_1    731 non-null uint8\n",
      "weathersit_2    731 non-null uint8\n",
      "weathersit_3    731 non-null uint8\n",
      "weekday_0       731 non-null uint8\n",
      "weekday_1       731 non-null uint8\n",
      "weekday_2       731 non-null uint8\n",
      "weekday_3       731 non-null uint8\n",
      "weekday_4       731 non-null uint8\n",
      "weekday_5       731 non-null uint8\n",
      "weekday_6       731 non-null uint8\n",
      "temp            731 non-null float64\n",
      "atemp           731 non-null float64\n",
      "hum             731 non-null float64\n",
      "windspeed       731 non-null float64\n",
      "holiday         731 non-null int64\n",
      "workingday      731 non-null int64\n",
      "yr              731 non-null int64\n",
      "cnt             731 non-null int64\n",
      "dtypes: float64(4), int64(5), uint8(26)\n",
      "memory usage: 70.0 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 合并数据\n",
    "X_train = pd.concat([X_train_cat, X_train_num, train['holiday'],  train['workingday']], axis = 1, ignore_index=False)\n",
    "#print(X_train.head())\n",
    "\n",
    "# 合并数据\n",
    "FE_train = pd.concat([train['instant'], X_train,  train['yr'],train['cnt']], axis = 1)\n",
    "FE_train.to_csv('FE_day.csv', index=False) #保存数据\n",
    "#print(FE_train.head())\n",
    "\n",
    "print(FE_train.info())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2:0.853814736719781\n",
      "MSE:621746.8036719494\n",
      "RMSE:590.9812536346217\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "from sklearn.metrics import r2_score#R square\n",
    "from sklearn.metrics import mean_squared_error #均方误差\n",
    "from sklearn.metrics import mean_absolute_error #平方绝对误差\n",
    "\n",
    "def load_data():#导入数据\n",
    "    global x_data,y_data,name_data    \n",
    "    data = FE_train\n",
    "    data = data.drop(['instant','hum','windspeed'], axis = 1)#去掉编号、湿度、风速等不相关数据\n",
    "##    print(data)\n",
    "    \n",
    "    y_data = data['cnt']\n",
    "    x_data = data.drop('cnt', axis = 1)\n",
    "\n",
    "    y_data=np.array(y_data)\n",
    "    x_data=np.array(x_data)\n",
    "    name_data =list(data.columns)#返回对象列索引\n",
    "\n",
    "##    print(x_data)\n",
    "##    print(y_data)\n",
    "##    print(name_data)\n",
    "def traintestsplit():#数据分割，一部分用于验证、一部分用于训练\n",
    "    global x_data,y_data,name_data\n",
    "\n",
    "    X_train,X_test,y_train,y_test=train_test_split(x_data,y_data,random_state=0,test_size=0.20)#分割数据，20%用于测试，80%用于训练\n",
    "\n",
    "    return X_train,X_test,y_train,y_test\n",
    "\n",
    "load_data() #数据导入\n",
    "X_train,X_test,y_train,y_test=traintestsplit()  #数据分割\n",
    "\n",
    "#使用线性回归模型LinearRegression对数据进行训练及预测\n",
    "\n",
    "#lr=LinearRegression()#最小二乘线性回归模型\n",
    "\n",
    "#lr=Ridge()#岭回归模型\n",
    "\n",
    "#lr=RidgeCV()#\n",
    "\n",
    "lr=Lasso()#Lasso模型\n",
    "\n",
    "#lr=ElasticNet()\n",
    "\n",
    "#使用训练数据进行参数估计\n",
    "lr.fit(X_train,y_train) #训练模型\n",
    "\n",
    "#R2评价指标\n",
    "lr_y_predict=lr.predict(X_test)\n",
    "score = r2_score(y_test, lr_y_predict)\n",
    "print(\"R2:{}\".format(score))\n",
    "\n",
    "#MSE评价指标\n",
    "mse_test=mean_squared_error(y_test,lr_y_predict)\n",
    "print(\"MSE:{}\".format(mse_test))\n",
    "\n",
    "#RMSE评价指标\n",
    "Rmse_test=mean_absolute_error(y_test,lr_y_predict)\n",
    "print(\"RMSE:{}\".format(Rmse_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwEAAAI1CAYAAACOg/YaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd3hUZdr/P9PSO4QiVcVEQIoFXhR+qOguiNgFZUERUIRd3FfFgnVX1n0V1waiCAqushYsoOhiR7FQBElEkCoQMZT0Okmmnd8fM+dkQtrUTMn9ua69NmbOM8+TIXnOuZ/7/n5vnaIoCoIgCIIgCIIgtBv0oV6AIAiCIAiCIAhtiwQBgiAIgiAIgtDOkCBAEARBEARBENoZEgQIgiAIgiAIQjtDggBBEARBEARBaGdIECAIgiAIgiAI7QwJAgShBVatWkV2djYDBgwI9VKiiu3btzN9+nSGDh3KwIEDGTVqFD/99FOolyUIQoQwd+5csrOzmT59ulfjNm/eTHZ2NtnZ2RQWFgZpdYI7eXl5zJ49m2HDhjFgwADOP/98Pvvss1AvSwCMoV6AIAjti+PHjzNlyhTMZjMAycnJFBQUcNJJJ4V4ZYIgCEIgMZvN3HjjjRw7dgyAxMRESktL6dSpU4hXJoAEAYIgtDHr1q3DbDaj0+l47bXXGDp0aKiXJAhChJGZmcnJJ59M165dQ70UoQW2bt2qBQDz58/nyiuvDPGKBHckCAhDDh48yBVXXMGQIUNYtmxZ2Mz3/fffM23atFbfb8+ePYFcnhBllJaWAtClSxcJAARB8Ik5c+YwZ86cUC9DaAV1v9fr9YwbNy7EqxFORIKAMKOqqoo5c+ZQV1cXdvPt2rULgNjYWJKTk4O9NCFKsdvtAMTFxYV4JYIgCEIwUfd7o9GI0SiPnOGG/IuEEWVlZfz5z39m586dYTmfGgRMmTKlXZ/AzJ07l9WrV/O///u/9O/fnyeeeIK8vDw6duzI1KlTmTJlCgAOh4NPPvmEN954g/3791NXV0dWVhaTJ0/msssuY8CAAVgsFr788ku6d+8esPVt3LiRlStXsn37dgoKCkhOTmbw4MFMmTKFYcOGNTnmu+++48033yQ3N5fy8nKSk5MZMGAA48eP5w9/+EOzc1VUVPDqq6/y+eefc/jwYRRFoXv37lxwwQXcdNNNdOzYUbv2ueeeY9GiRdp/Hzx4kOzsbAAee+wxrr766gB9AoIgRDqrVq3ivvvu49xzz+WRRx7hwQcf5KeffiIhIYERI0ZgNBpZvXo1I0aMaDKD/fHHH/PGG2+wd+9erFYrAwYMYObMmej1LfuhlJSU8Oqrr/LFF1+Qn59PQkICw4YNY/bs2ezatYs777yToUOHsmLFikZjDx06xPLly/n+++8pKCggNjaWPn36cOmllzJhwgRiY2MD9vmo7NixgzfeeIMtW7Zw7Ngx4uPj6d+/PxMnTuSPf/xjk2O2b9/Oq6++ytatWykuLiYxMZHs7GyuuOIKrrzySgwGQ5Pj6urqeOONN1i7di0HDhzAarXSpUsXhg8fzrRp0+jRo4d2rfrvp2KxWLT9fvbs2dx2220B/BQEX5EgIEzIyclhzpw55Ofnh+18u3fvBqBv377BWlZEkZubywsvvIDNZiM+Pp6jR4/SrVs3AGw2G/feey8fffQR4DwFiY+PJzc3l9zcXLZs2RLw9TgcDv7v//5PuznpdDqSk5MpKSlh3bp1rFu3jr///e9MnDixwZgHH3yQ9957TxuTkpJCWVkZ69evZ/369YwZM4Z//etfxMTENJhv9+7d3HLLLRQUFADODJHBYGDfvn3s27ePd955h+eff55zzjkHgISEBDp27IjZbMZsNmMwGEhPTwckKyAIQtOUl5dz0003ceTIEU1Ump6eTmVlZZPX2+12HnzwQVatWgXU772bNm1i8+bNXHrppc3OdeDAAaZNm8bRo0cB555VWVnJf//7X7788ksuv/zyZseuWbOG+++/H6vVCjgFsLW1teTk5JCTk8O7777L0qVL6dy5s68fRSNeeuklnn76aRwOB+A0WaioqGDDhg1s2LCBGTNmNDqwW7hwIS+88AKKogCQkpJCVVUVmzdvZvPmzaxevZrnn3+e1NTUBuOOHDnCzTffzK+//gqAyWQiNjaWvLw88vLyWLVqFfPnz2fMmDGAc0/v2LEjtbW1VFVVAWiHQgkJCQH7DAQ/UYSQUllZqdx1111Kdna2kpWVpfzhD39QJk2apGRlZSnTpk0Lm/lqa2uVvn37KllZWcqBAwcCvq5w5b333lOysrKUM844Q/vevffeq2RlZSlZWVnK1VdfrRw9elRRFEXJyclRrFaroiiK8txzzylZWVlK3759lWXLlilms1lRFEXZvXu3Mn78eG18VlaWcvjw4YCs9dVXX9Xe84knnlBKSkoURVGU4uJi5e6771aysrKUfv36KT///LM25oknnlCysrKU7Oxs5cknn9TGVFRUKC+++KL2b/7ggw82mKu4uFgZMWKEkpWVpcyePVvZs2ePYrfbFUVRlF27dinTpk1TsrKylCFDhihHjhxpMHbhwoVKVlaWMnr06ID83IIgRB/q3puVlaUMHTpU+fHHHxVFUZTDhw8rR44c0fbhE+9by5cv1/a0JUuWaHvvnj17Gu29BQUF2jiLxaKMGTNGuy9u2bJF+/7q1auVwYMHa+MmT57cYM4tW7Yoffv2Vfr166c8+eSTyvHjxxVFURSr1aqsX79eufjii5WsrCzl2muv1fZJf/nyyy+19cydO1e7D1VWVirz58/XXvv888+1Ma+//nqDMceOHVMURVFqamqUt956Sxk0aJD2mTocDm1cbW2tctlll2k/u/u9Li8vT7nzzjuVrKwspX///sqOHTsarLOpe6gQPkifgBBz+PBh1qxZA8B1113HqlWrPC4NOXToEA8//DAXXXQRAwYM4JxzzuH6669nxYoVzdb4+zrfnj17sNvtJCQk0KtXLw9/uuhn3rx5dOnSBYDBgwdjNBopKyvj5ZdfBuDuu+9m2rRpxMfHA5Cdnc0rr7wS8M/QYrGwYMECAKZPn87dd9+tnbJnZGTw2GOPccYZZ2Cz2Xj33XcBOHr0KK+++irgTM/OmTNHG5OcnMytt97KAw88AMDbb7+tZYLAeQJVUFDA6NGjee6558jKytLS7KeffjpLliyhb9++lJeXs3Tp0oD+rIIgtC9mzpzJWWedBUD37t2bdQSqq6vj+eef18bMmDFD23uzsrJYvnx5s1bE7777LgcOHCAxMZFXXnlFy2CaTCauvPJKbX9tisceewy73c7999/PnDlzNPtLo9HIyJEjeeWVV0hISGD79u18+umnvn0IJ/DEE08AcMkll/DYY49p96GkpCTuuecerYxz5cqVgNOqU/0Zrr76ah577DEtKxEXF8d1113Hs88+CzjLQ7/66ittrnfeeYc9e/YwePBgXnnlFe1eB9CzZ0+eeuopLrzwQqxWa4ufkxB+SBAQYnQ6HaNGjWLVqlXMmzePpKQkj8atWbOGcePGsXLlSn7//XdMJpOWenz00UeZMGECx48fD9h86gNgdnY2mzZt4s4772T06NFcdNFFTJ8+ndWrV2spyfZCWloa/fv3b/T9r7/+mpqaGtLS0pg0aVKj1xMTE7nlllsCupaNGzdSVVWFwWDg5ptvbvS6wWDgzjvvZM6cOdrN4YsvvsBqtZKUlNRsw52JEydqN1y1tAngww8/BGD8+PFNjjMajVxzzTUAfPnll77/YIIgtHvOO+88j67bvHkzlZWVGI1GbrrppkavJyUlceONNzY59uOPPwbgyiuv1Mo63Rk5cqQWiLhz4MABduzYgU6n0/a8E+nevTsjRowAArMf7t+/n4MHDwIwY8aMJq+ZOXMmf/3rX7U9etOmTZSVlaHT6fjrX//a5JgLLriAM888E6jf492/vuqqq5oV906YMAGADRs2UFNT48NPJYQC0QSEmNNPP53Fixd7NWbr1q3MnTsXnU7HjBkzuOGGG+jUqRM2m40NGzbwj3/8g927dzN79mxWrlzZQAjly3xQLwr+5ZdfmDp1aoPXfv/9d7777jtWrVrF888/T0pKitfvH4k0d5q/bds2AAYOHNiojl6lOYGur+zYsQOA3r17k5GR0eQ1w4cPZ/jw4dp///zzz4Azg6Gelp2IXq9n6NChfPDBB9oc+fn5WqfNu+66q9mbgsViAZzNwaqqqjwOOAVBENzp3bu3R9dt375duz4tLa3Ja9QT/hPJyclp8XVw7tvq/q7i3un8oosuanZsdXU14Awa/EXdixMSEprV6J1xxhmcccYZ2n+r+32vXr1a7K1w7rnnkpOTo81hs9k085CnnnqK5557rslxNpsNAKvVym+//aaJgIXwRoKACERNPT788MMNTprdU4+XXXaZlnq85JJL/J5TDQLq6uqYOHEiN9xwAz169KCwsJDVq1ezePFifvjhB+bMmcNLL73k93yRQHMPtapQNjMzs9mxgRSHARQXFwM0cONpjZKSEoBWOzeqa1WvV+cCp8OUJ1RUVEgQIAiC16gCVE9Q96YOHTo0e01Te29paal2aNHSftjU2KKiIgAURdG+bony8vJWr2kN9efMyMhAp9N5NMbX/b68vFwTO1dUVHg0l6fXCaFHgoAIw5vU42effcaXX34ZkCBAjeovueQSzQIToFu3bsyePZsePXpwzz338M033/DNN98wcuRIv+cMd5rbfNUNU3G5LzRFS6/5gnoK4w2erkEt81J/XtX3GZzdf5tKnQuCIAQCTx9yPb22qcyl+/7ZUllrU3umen23bt1Yt26dJ8v0G/c92FMCsd+vWLFCGjxGGRIERBihSD0CPPLIIy2+fsUVV/Dyyy+zd+9ePv7443YRBDSHehqvlsw0RUuv+YIq6FVPb5qipqaG48ePc9JJJxETE6OVDTWlHXFHfV29Xp0LaGCLKgiCEErUvVfNxjZFU3tkamoqRqMRm83W4ml+U6+p+2FRURE2m61NGmKppU4lJSUoitJk8GO32zl06BDdu3cnNjZW279b+myg8X6flpaGXq/H4XBo1qlC9CDC4AjjxNRjc/9ThTmBSD16ypAhQwA0H+H2ysCBAwFnDaaaFTiRrVu3BnTOfv36Ac4GXM2V6Hz66aeMHj2ac889F7vdzoABAwBnv4PmhFx2u50ffvgBcOpJwOkGoeo+vv/++2bXtGDBAkaPHs1f//rXgGc+BEEQTmTw4MEA5OXlNfuwq9b+uxMTE6PV1p9Y8+9OU/u2WndfV1fXYv+XqVOncvnll/PCCy80/wN4iLrfm81m9u7d2+Q1OTk5jB07lkGDBlFcXKzt93l5eRw5cqTZ9960aRNQv9/HxMRw2mmnAS3v92+//TYXX3wx06ZNw2w2e/9DCSFBgoAIwz31uGfPnlb/15bOLMnJyQDU1ta22ZzhyCWXXILJZKKsrIx33nmn0esWi4Xly5cHdM7hw4eTlJSEzWbjtddea/S6w+HQ1jJ8+HAMBgN//OMfMZlMVFdXa5amJ/LWW29pJ0Nq90m9Xq+VmL3++utNng4dPXqU//znPxw6dIj09HSvUvqCIAi+MHToUDp27IjD4WjSAMNisWi2yCeiNgL74IMPmgwgtm3bxubNmxt9v2/fvpx88skAPPvss00e/Kxbt44NGzawZ88ej0XOLdGvXz+tO29z95K33npLu7ZDhw6cd955pKWloSgKCxcubHLM119/TW5uLkCDbsNqg7W1a9dqImF3KisrWbp0KYcPH0av10szsAhCgoAI48TUY1tw+PBhXnvtNZ555hmt819TqFmKlgSx7YGMjAymTZsGoHXwVQOjgwcPcuutt7Jnzx7t+kA8ICclJXHrrbcCsHjxYl544QXt36qgoIC5c+eydetWjEYjs2bNAqBLly7ccMMNADz//PM8/fTTlJaWAlBVVcXSpUv55z//CcA111yjWceB034uNTWV8vJyJk+ezHfffYfD4UBRFLZu3cr06dOpqKggJSVF+ywEQRCCidFoZO7cuQC88cYbPP7445pI9bfffmPmzJns27evybETJkygR48eVFRUcNNNN2kZAbvdzmeffcasWbOazGjqdDruuecewJlVveWWW9i/fz/gDDo++OAD7r77bgD69++vWTT7g16v58477wTg/fff5x//+EcDIe8TTzyh2XredtttAMTHx2vWoKtXr+b+++/XDnjq6up4++23uf322wGnJevYsWO1+SZNmkSPHj2wWq1Mnz6dtWvXasGO2jn+8OHDmEwm/vznP/v98wlth2gCIowTU4/nnntuk9dNnTqV4uJixowZ4/cfZWFhofYwOHDgwCa1CA6Hg40bNwI0eFhsr9x2223s2bOHr7/+mkcffZT58+cTHx+v3ZBGjRqlichMJlNA5rzlllv4/fffWblyJQsWLGDRokVa23t1nkcffbSBpdycOXMoLi7mgw8+YMmSJbz00kskJydTVVWlCcIuvfRSHnrooQZznXTSSSxevJi//OUv/P7770yfPp24uDigPhMUFxfHwoULpbmcIAhtxmWXXcahQ4dYtGgRr7zyCq+99hpJSUlaaewNN9zAihUrGo2Li4vj+eef58Ybb+TXX39l4sSJJCYmYrfbqa2tJSUlhSFDhrBly5ZG1s+jRo3i4Ycf5p///CcbN27k0ksvJSkpiZqaGm0f7datG4sXLw7Yfj927Fjy8vJ49tln+c9//sPrr79OcnKydo/R6XTccccdXHjhhdqYSZMmcezYMZYuXcp7773He++9R2pqKtXV1dqh4rBhw3jqqacaWIsnJSWxZMkSZsyYwe+//84dd9yByWTCZDJppT8Gg4FHH320yV4KQvgimYAIIxSpx4EDB2qCq2XLljXpnvD666+Tn5+PyWTiqquu8nvOSMdkMrF48WLmzZvHoEGDiImJwWazcdZZZ/Hcc89pp/HgbB4WCHQ6HfPmzWPp0qVceOGFpKamUlNTQ2ZmJuPGjePdd9/lyiuvbDDGaDTyxBNPsGTJEkaNGkV6ejpms5nMzEwuuugilixZwtNPP91kH4Gzzz6btWvXMmvWLE4//XT0ej12u53u3btz3XXX8cEHHzQbpAqCIASL2267jVdffZULLriAtLQ0LBYLAwYM4Omnn9Yypk2RnZ3NRx99xA033ED37t2xWq0kJiZyxRVXsHr1avr06QM0vWdPmjSJNWvWMGHCBHr27InVasVkMpGdnc3s2bP54IMPAm4NPWvWLFauXMnYsWPJzMzUmlSOGjWKFStWNPmzzpkzhzfffJOxY8fSuXNnzGYz6enpDB8+nCeffJJXXnmlyV4zp556KmvWrOGuu+5i0KBBxMXFYbVa6dy5M+PGjeOdd95pdH8Rwh+dIoq9sGPu3LmsXr2aESNGsGzZskavr1u3TnuIPPfcc3nwwQfp06cPFouFjz/+mHnz5lFVVUX//v1ZuXJlqycPrc0HzvrCv/3tbwCMGTOGe+65h27dulFVVcWKFSt47rnnsNvt3HbbbcyePdvPTyD6+eqrr7SSGlV4KwiCIIQvf/3rX/n000+56aabuO+++0K9HEHwGykHikBCkXq8/vrrycvLY/ny5XzyySd88sknjeacNGkSf/nLXwIyXySzceNGbr/9dnr27Mlrr73W5Cn6+++/D9S7WQiCIAih47777mPTpk1cccUVWm28O8XFxXz77beA7NtC9CDlQBFKKFKP9957L//+97+5+OKL6dChA3V1dWRkZHDRRRexbNkyHn74YXGBAQYMGEBdXR3bt2/n3nvvJS8vT3vt2LFjPPLII3zyySfo9XpuvvnmEK5UEARBADjrrLM4cuQIL7/8Mq+//rpW6+5wONi6dStTp07FbDbTp0+fFnv0CEIkIeVAghAEVq1axQMPPKDpJ+Lj49HpdNqNxWQycd999zFp0iTAqbXw1Tb0kksu4cEHHwzMwgVBENohNpuNW2+9le+++077XkpKCmazWRPN9uzZkxdffJFTTz3Vr7nWrl2rmW14y5lnnsmiRYv8ml8QVKQcSBCCwNVXX82gQYNYsWIFP/74I0ePHsVut9O7d2+GDRvGxIkTtWYs4Gz60lKnypZoybZVEARBaB2j0chLL73El19+ydtvv82BAwcoKCggOTmZnj17MmbMGCZMmEBSUpLfc9XW1vq837dlA1Ah+pFMQBvz448/Ak47LUEQhEhC1f+cffbZIV5J2yF7tiAIkUpre7ZoAiIEu92u/WMKQrQhv99CtCG/00I0I7/f0YGUA7Ux6mmSt+4CaofZ7OzsgK9JEEKN/H5HBrm5uaFeQpsje7YgNEZ+vyOD1vZsCQIEQRCERlRWVrJixQq++OILDh06hMViIS0tjX79+nHXXXeRlZXV5LiDBw+yePFiNm7cSGlpKenp6QwbNowZM2Zw2mmnNTufw+Fg5cqVvPfee+zfvx+dTsfJJ5/MVVddxZ/+9KcWy3F8nVMQBKE9I0GAIAiC0IBDhw4xbdo08vPzAYiNjcVoNFJYWMj69evZsGED//znP7niiisajNu+fTtTpkzBbDaj0+lISkqioKCANWvW8Omnn/L0009z8cUXN5pPURTuvPNOPv74YwDi4uJQFIWdO3eyc+dOPv/8c15++WViYmIajfV1TkEQhPaOaAIEQRAEDZvNxp///Gfy8/Pp3r07y5cvJzc3l5ycHP71r3/Rr18/rFYrDzzwADt37tTGlZeXc+utt2I2m7nwwgtZv349W7du5ZtvvmHUqFHU1dVx9913a4GFO4sXL+bjjz8mISGBZ555hm3btpGTk8PTTz9NYmIimzdvZv78+Y3G+TOnIAhCe0eCAEEQBEHjk08+4ddff8VgMLBo0SKGDx+OXq9Hp9PRs2dP7rvvPk455RSsVitLlizRxq1YsYKSkhJ69OjBwoULtWaFnTt3ZuHChfTv3x+z2cyLL77YYL6qqiqtR8b999/P2LFjMRgM6PV6Lr30Uh5//HEA3nrrrUYP877OKQiCIEgQIAiCILixfv16AP7nf/6Hvn37Nno9JiZGKwPasmUL4CzneeuttwCYPHlyo7Idk8mkdcdeu3YtFotFe+2jjz6isrKStLQ0rrrqqkbz/fGPf+SUU07BZrPx3//+V/u+P3MKgiAIEgQIgiAIbvTr14/Ro0czcuTIZq/JzMwE6hvVHThwgMLCQgCGDRvW5Jhzzz1XG5OTk6N9f/PmzQAMGTIEo7Fpmdp5550HwLfffqt9z585BUEQBAkCBEEQBDemTp3KwoULmTp1arPXbNu2DYAuXboA8OuvvwJojj5NkZ6eTlpaGgD79+/Xvq9+fcoppzQ7X+/evQHYt2+f9j1/5hQEQRDEHUgQhBBjdyjc/9kRdhyvQa87GOrlNODSgV15cvygUC8jrCgoKOCjjz4C4Pzzz9e+B86H7tjY2GbHdurUibKyMu1697FqPX9z4wBKS0uxWq2YTCa/5hQEQYgkPtlxlMc/3s3/XTWA8/p0DNj7ShAgCEJIOVhUxY/5Ztd/hVcHynd//J1HLu9PYqxslQAWi4UFCxZQW1tLbGws06ZNA+rLguLj41scHxcXB0B1dbX2PfXrlsaq49Tr09LS/JrTW+x2u9YcyVPMZufvtLfjBCESkN/vtuXf649wqNjMuxv30MFe7PE4u93eYo8VubMJghBSiqvqBZsv33gO+jAoUiyqsnDPu9sBKKm2SBCAMwB4+umntbKaBx98kJNOOglw2oqCU4zbEqp4V73e07Huol+73e73nIIgCJFEWa1z30uODewNUu5sgiCElFKzMwhIjNFzcb/mS0LakspaqxYElJot9MhICPGKQkt1dTWzZ8/WxLVTp05lwoQJ2uvqabvVam3xfVSHHvcH97i4OGpqaloc6+7so471Z05vMRgMZGdnezVGPSH1dpwgRALy+922mD84AsDpvbuRnd3T43G5ubktvh4GZ26CILRniqudD2kpsc2nLNuapFgjJoMOqF9fe6WgoIDJkyezYcMGAMaOHcvcuXMbXJOYmAhAbW1ti++lvp6UlOTVWPU1nU5HQkKC33MKgiBEEsVVdQB0SGzcNd0fJAgQBCGklLoestPiwmc70ul0ZLg229J2HATs37+f6667jl9++QWAa6+9lilTpjS6rmvXrkC9cLc5jh8/DtQLfaHeYagl4a46rkOHDpqNqD9zCoIgRAq1VjvVFmc5UIckCQIEQYgitExAXPhkAgDSE5ybbUk7DQJyc3P505/+xJEjRzAYDDzyyCOMHz++yWtPPfVUABwOB7/99luT15SWllJeXg5Anz59tO+rXx86dKjZteTl5TWYx985BUEQIgX3bHSHxOad0HxBggBBEEKKetKeGmZBgHri0h6DgN27dzNjxgzKy8uJjY1l4cKFXH/99c1e36NHD7p16wbApk2bmrxm48aNgLOWf+DAgdr3hw4dCsDWrVs10e+JqKVI6rX+zikIghApqKVAIJkAQRCijBKzs5QjNYw0AVCfCVCFy+2F6upqbrvtNsrLy4mLi2Pp0qVcfPHFrY675JJLAHjttdca1elbrVZefvllAK666qoGvv4XX3wxsbGxFBYWsmrVqkbv+9lnn3HgwAFMJhPXXnttQOYUBEGIFFQHvRijnqQAO9VJECAIQkgpqXaecoRdJsClCXC3MG0PvPjii1p5zd/+9jeGDRvm0bjp06eTmprKoUOHmDlzJocPHwacNfm33347O3fuJCEhgZtvvrnBuNTUVKZPnw7AP/7xD959911sNhsOh4OPP/6Y++67D4AJEyZo+gF/5xQEQYgUitxEwTqdLqDvLRahgiCElNJqZyYg7DQBie0vE2CxWHjjjTcApzj6qaee4qmnntJeVwW47nab33//PQAZGRk888wzzJo1i40bN3LxxReTkpJCZWUliqJgMplYsGAB3bt3bzTvrFmz2LlzJ+vXr+eBBx5g3rx56PV6ampqABg2bFgjRyJ/5xQEQYgE1JLUQJcCgQQBgiCEmGI1ExBm5UCqO1B7sgjdu3ev1olXURSKioq8Gj98+HDWrFnDiy++yIYNGygpKSE9PZ0hQ4Ywc+ZM+vXr1+S4mJgYXnzxRT9WzIkAACAASURBVFauXMmqVavYv38/NpuN7OxsLr/8cm688cYGDcMCMacgCEIkoN6DAi0KBgkCBEEIITUWO7VWBxB+5UDt0SL0jDPO0JoANYXaeGbw4MHNXtO7d28ef/xxr+fW6/VMnDiRiRMnej3W1zkFQRDCnaIg9QgA0QQIghBC1CwAQEoY9QkAyHAJg8tqrNgdSohXIwiCILRHVF2alAO1IWvXruX1119n165dOBwOunfvztixY5k2bZrWrl4QBP9Q9QAAaeGWCXBtuIoCZWYLHZLEXUYQBEFoW+o1AYG/B4XX0VuYMH/+fO644w62bt2KxWJBp9Oxb98+FixYwPjx4ykrKwv1EgUhKihxiW6Nekgwhdd2pGYCoH2JgwVBEITwoVjKgdqONWvWsHz5cgwGAw899BDbtm0jJyeHZcuWkZmZyd69e5t0qRAEwXtUe9CUWEPArc/8Jd1tw21vNqGCIAhC6FEUhaIgugNJEOCG3W5n0aJFANxyyy1MnjxZc6QYMWIEixcvRqfT8dVXX7Ft27ZQLlUQooISVzlQuImCAUwGPclxzopJyQQIgiAIbU21xY7F5jTPCIY7kAQBbmzYsIG8vDz0ej1Tpkxp9PqAAQMYMWIEAO+//35bL08Qog73TEA40h5tQgVBEITwQC0FAhEGB53NmzcDcPrpp5ORkdHkNeeddx7ffvst3333XVsuLeywOxQKK+twKOKaEm50SYlDrw+v0prmCOdMADiDgLxic7uyCRUEQRBaZvvvZdy/+mcuyOrEXaOzgzZPkVspqvQJCDL79+8H4JRTTmn2mt69ewOQn5+P2WwmISGhLZYWMmx2B3klZvYdr2Lf8Ur2FVSxr6CKXwurtBSVEF4M7Z3ByluHhV2NfVOomYCwDQISJBMgCIIg1PPtvkJmrviRaoudg4XVQQ0C1ExAQoyB+JjA3yclCHCjoKAAgM6dOzd7TadOnbSvCwsL6dWrV/DXVVHLy1uKMFsdpP3SNg8j5TVW9hdUcaCwGotdHvYjiR8OlVBncxBnCs8Ha3dKtUxAeFYmBqph2PGKWqyt/B3FGg1kJreNDamiKBHzOyIIghAurPnpCHPezsVqd1ZBVFvs1FjsQXlAB3d70MCXAoEEAQ2orq4GID4+vtlr3F+rqqryaR673d5iV84TeT23hHd2qLakFT7N6S+xRh09U2PomRZDrzTn/3dLMWGMkLKT9kB+hYUHPz8KwE+/7Ak73/2mOFZaCUC83oHZbPbq76JNqHP+jR8uLPN5bUt+KGTVznKPrr1lSAeuPSPdp3m84bGvj7H5cDXPXd6DHqme31zsdjsGQ/j/XgmCIASaV74/yCMf/gJA19Q4jpbXAs6ml91jglMVomahg1EKBBIENMBmswFgMpmavUZ1CwLnDbEtuOjUZI6W12K2OjAY2uafLNao0x74e6XF0CnJiD4CykvaMwa3w/QaqyMigoDyWuffUEpseGYC1DIldZ2+8MNhs8fX/phvbpMg4IffzdTYFPJKLV4FAYIgCO0NRVF48rM9PP/VrwCc0yudZ68fzIj5XwHO0/ru6cEJAoqC2CMAJAhogNoJ2Gq1NnuNxVJfFtBSsNASBoOB7GzPa8iygS7JzlNIb8YJ7YvOZguQB0Cnk3qSfVJKaBfUCnaHQqXFqcPJTIknISEh7H6/syoPw9Ziaux6n9dWVncIgH9edQYjT8ts8pq3tvzG81/9Sh2moH8GFpsDs9X5uQ/IOpnsk5s2QWiK3NzcYC1LEAQh7LDZHTywegcrtx4G4OK+nVn0pzOJNeqJMeix2B1B7SMj5UBtSGJiIgC1tbXNXlNTU6N9nZSUFPQ1CYKnJMbW/zlXW2whXIlnlNdYcbjMpVLCNGtRLwyua+XKpqmus1FV5/y36Ns1hR4ZTZ8W9e7g3HtK2kCA7N7zICPRt4MMQRCEaKfWamf2Gzl8ses4ABPO6c7/XTUAoyvt3iEphqPltdppfTBQA4wOScEpBwrPHHyI6NKlC1AvEG6K48edvww6nY6OHTu2yboEwRNMBj0xRueftPrgGc64P/CGa+lShuv0pdbqoMbifUlQQWX9zaFTC6Jf9ZSnpNqCEmTbXffPPSNIdaaCIAiRTLnZyg3LNmsBwF8uPJX51wzUAgBouG8Hi2CXA0kQ4EafPn0AOHToULPX5OU5yy26devWooBYEEJBosuhoDrCgoDkcG0WllC/8fqSDSioqM8qtuT8k+6ap87moMYaXK2R6nSk00FqvGQCBEEQ3CmqqmPCko1sOVQKwN8u68fdo09vZLutHqIE00K6OMjlQBIEuDF06FAAfvnlFyoqmnbh2bBhQ4NrBSGcUEuCIikISI41EmMIT9F5htvGq9qZeoOaCUhLMBFrbD7QcXd+CGZ9KdTfVNITYjCIu5cgCEID/v39IfYcr8Rk0LFw4plMHX5yk9d1dJ3OB6scyOFQtEObYLkDSRDgxpAhQ8jMzMRms7F8+fJGr+/YsUPrFHz99de39fIEoVWSXEFAVV3bOFf5gxoEpAcpzRkIkmONmFwBSonZ+4dzNQhoqRQIIN2tNr/Uh3m8QX3/9ATJAgiCIJzIr4VOa+hrzurO5YNOava6YJcDVdRasbmEc5IJaAMMBgO33347AEuWLGHp0qXU1Tlv4hs3bmTWrFkoisIFF1zAoEGDQrlUQWiSSMoEqA+jGWEcBOh0Oq1Up8SXcqBKZzlQ55S4Fq9Lcgs2gt2dWBOaiR5AEAShEUfKnAYwzRk5qGjlQEHK3ha5va/0CWgjrrnmGnJzc3nnnXd46qmnWLhwISaTCbPZ6fWdlZXFk08+GeJVCkLTRFIQoJ6ehHMQAM71FVTWUeJLOVCFM3BorROwTqcjIzGG4xV1fncnbg0tEyDOQIIgCI3IL3Me3pyU1vLhjXo6XxykcqCGJg6SCWgTdDodjz76KM888wxDhw4lLi4Oq9VK7969mTlzJm+++SbJycmhXqYgNEmSS2AbSe5A6QnhHwSAf5mATskt30wAt4xD22gCwj34EgRBaGtqrXatxv+k1JbNXzqqQUCQXN3U4CIlzqg5/wUayQQ0w9ixYxk7dmyolyEIXpEY4/yTNvtgZ9nWBLsJSqBI14IA3zMBrWkCoG3s5qDeHUiCAEEQhIYcK693dOuW3nIQoJYD1dkcVFvsmiYvUBRp98jglW5KJkAQoohETRgsmYBAkeGXJsAVBKS0vomrn0OwhcGR8rkLgiC0NfkuPYBe17qWy927vyQIuoDiIPcIAAkCBCGqSIpATUAwN7hAoJ6Ye2sRWmu1U17jHNPazQTqP4dgW4RGSgZGEAShrVGDgM4pcZgMLT8iu++hRT52lW+JttirJQgQhCgiEoXB4WwRCvVBgLfNwgo97Bason4OwcwEKIriZhEa3p+7IAhCW6M6A52U1noz2IQYI/Empw4vGIc3mpOblAMJguAJkSIMrrHYtc644V6brmUCzN5lAlRRMHgmDK4PNoIXBFTW2bDaXb7TYhEqCILQAG+CAHDXcgU+E1Ak5UCCIHhDfSYgvIXB7qfdkRIElJkt2B2eO0CoouDkWCPxMc13Cz5xnmBahLq/t1iECoIgNOSIh/agKuopfVEQMgFtUTIrQYAgRBGRUg7UwP84zMtS1Idzh4JW4+8Jqig40wNRMNR/DmU1Vq+CDW8obgPfaUEQhEhFzQR09zQTEEQtV7G4AwmC4A2aMNhiC4pvcaBQgwCDXkdKfHg7Fbs/LHtj31nfI8DDIMCVVlYUZ9YhGKiZgDiTnoSY8P7cBUEQ2hJFUTRhsMflQH70kWkJm92hZcwlEyAIgkeomQCHglZzH46421TqdLoQr6Zl0hLqy2a8CQKOu8qBPHEGgoYZkWCJg7UuzWGefREEQWhriqst1NkcgDeagFhtbCApNVtRz/EkEyAIgkeowmAIb3FwpNiDAsQaDSS7givvMgGeNwqDhi5JwbIJ1YIAsQcVBEFogFoKBN5nAgKtCXC/14hFqCAIHpHo1rEwnMXB9fagkSFOre8a7EUQUKGWA3mWCTAZ9CTHOf/9gpYJEHtQQRCEJlGDgKRYIylxnpVLBssdSG0UptMFd7+WIEAQooiGQUAYZwK0WsfIsKnM8MHDv9CLbsEqHYJsE6p2tYyEDIwgCEJbku/mDORpmapWDlRlCagOr8itZNagD17JrAQBghBFJLqJPcO5HKg0wjIBGV5mAqx2h/Ygn+lhORC4NQwLUhCgNQqTIEAQBKEB+aXOTEA3D0uBoP5AxeZQqKgJ3D23uA16BIAEAYIQVRj0OuJMzj/rcM4EFEeYQNXbIEBt8gKeC4Oh/vMIViagOIK0GIIgCG2Jt43CoGG9vrdd5VtC080FWb8lQYAgRBmqTWgkZAIixave2yBAdQYCz4XB7vMELRNQLZkAQRCEpjhS7n0Q4H4PC+ThTVFV25TMShAgCFFGJHQNLomwh1FVmOVpEKCKguNNBi0o84SMIGsCIi0DIwiC0FaomQBvyoHc3eOKqwKXCdDKgSQTIAiCN6i6gHAtB3I4FLcmKJEhDO7gZSagwE0U7E0fBF8EyJ5itTuorLU1mEcQBEGAWqtdO333JhMA9Q/qgTy8qbfRlkyAIAheEO7lQOU1VhwuE4VIEQZ7axHqbY+AE+cprbZ6Nc4T3AMLCQIEQRDqOVpeq319UprnOi5o6BAUKIrbSBMgfeMFIcpIdDUMC9dMQInbw2ikZALUh+Yaq50ai534GEOL1xdWunoEeCEKBneL0MB6TkPDAMaXIODgwYNMnjyZfv368fbbbzd7XUFBAS+88ALffPMNBQUFpKamMnjwYKZNm8bZZ5/d4hxr167l9ddfZ9euXTgcDrp3787YsWOZNm0acXHNf5b+zCkIgqA6A+l10MXLfVsr4wxgOVCRuAMJguALmibAEqZBgNvDaFpCZGQC3B+aPSnVUYXBvmYCaq0OaiyB1XSon7tOB2leagKqqqqYM2cOVmvLGYrDhw9z1VVX8eabb5Kfn09cXBwlJSV88cUXTJ48mTfeeKPZsfPnz+eOO+5g69atWCwWdDod+/btY8GCBYwfP56ysrKAzykIggD1eoAuKXEYDd49GncMcDlQnc2ulW6qWYZgIUGAIEQZ9eVA4SkMVh9GE2MMxJlaPlEPF9yDAE9KggoqvesWrM2TEBy7Oahfd1q8yavmM2VlZcyYMYOdO3e2eJ3NZmPGjBkUFRUxaNAgPvnkE7Zu3cqGDRu47rrrcDgcPProo/z000+Nxq5Zs4bly5djMBh46KGH2LZtGzk5OSxbtozMzEz27t3L3LlzAzqnIAiCSr4P9qAqakY7UOVA7uWgIgwWBMEr6t2BwjMToNmDBnlzCyQpcUbtwdmjIMDHTID7ZxJoXYAv9qA5OTlcffXV/Pjjj61e++GHH3LgwAGSk5NZsmQJJ598snO+9HTmzZvHqFGjsNvtLFiwoME4u93OokWLALjllluYPHkyMTHONY4YMYLFixej0+n46quv2LZtW0DmFARBcMeXHgEqGQEu43TvMyPlQIIgeEW4BwGRaFOp0+k8tgm1OxRtE++U4l0QkBxrxGRwBhuBzgR487lXVVVx9913M3HiRPLz8+nVqxdDhgxpccxbb70FwJVXXkl6enqj12fOnAnAhg0bKCgo0L6/YcMG8vLy0Ov1TJkypdG4AQMGMGLECADef//9gMwpCILgji89AlTU03pPjSNaQ92rjXodKXHBLZmVIEAQoowkVRgcppqASGsUpuKpTWhxdZ3mfuRtOZB7sBFom1BvPvfDhw+zZs0aAK677jpWrVpF9+7dm73ebDazfft2AM4999wmrxkwYADJyckoisJ3332nfX/z5s0AnH766WRkZDQ59rzzzgNoMM6fOQUhkvggN5/rlmwkr7g61EuJWlRhcDcvnYEAOrrq9kuqLdjVzd8PSlwHQBmJMei9KN30BQkCBCHKCPdmYZHWKExFtTNtLQgocOsW3NnLTAC4dycObDlQidna4P1bQqfTMWrUKFatWsW8efNISkpq8fqDBw/icDgAOOWUU5q8Rq/X07NnTwD27dunfX///v0tjgPo3bs3APn5+ZjNZr/nFIRI4sX1B9h8sIQVG/NCvZSoxOFQOOKyCO2W7ns5kEOBsgAc3qjagmCLgkGCAEGIOsK9T0CJ1igssoIAVfxV0somr4qCY4x6UuO9T+XWBwGBFgbXny61xumnn87ixYvp16+fR+/tXmrTpUuXZq/r1KkTAIWFhY3Gdu7cudVx7mP9mVMQIomjrlKVLYdKQryS6KS42oLF5jxQ8KccCAJTEqQ2LWuLe6T0CRCEKCPcOwZHeiag1MNMQGaSd92C6+cJUiag2vNMgLdUVVVpX7fk56++5n59dbWzxCE+vvmbr/tr6lh/5vQWu93Onj17vBqjZiy8HScI7tTZHJS5sng/55eTs2MXCabQn99G0+/3nsL6RmHmwt/ZU+6da517CVDOrv04yhL8Ws+BI84DDpOj1u/P1263YzA0//OE/jdJEISAopYDmS12HAGoTww0JREoDAbIUG3gWgsCKn0TBWvzJIQ+E+Atdruz9MxkMrUY+KiuP+r14LT5VMe2Ns59rD9zCkKkUGSuP8xxKLCroLaFqwVfKKh2fsYJJj2JrTSCbAqDXkdyrPNxuqzW/32m3PUeaXHBt9CWTIAgRBlqORA4xcHJQXYX8JZIFQZnJHiYCdB6BPgYBLg+l0BahCqKor1fMDIwsbHOn9VqtaIoSrMP5RaL87Nzf+BXT+pbakSmjnMf68+c3mIwGMjOzvZqjHqC5+04QXCn5Ndi4Dftv49YE/hTGPxORdPv97cFB4Bj9MhI9Pnn6ZRylMrCauJSO5Kd3duv9dR+UQSY6dOjC9nZffx6r9zc3BZfl0yAIEQZibH1pwfhJg6utdqpdnXCjbggwM0BoiXUcqDOXrae1+YJsOc0OPUhFruz5jUYGZjExETt67q65tddW+sMkNyFxupY9bWmqKmp0b5Wx/ozpyBECscqahr89w8HRRcQaOobhfm2Z0O9iLcoAA3Dil0W022hCZAgQBCiDPdMQLiJg91tLyMuCHCz7mypzOp4pW+NwrR51EyAOXCZAPesQjA+965du2pft+THf/z4caCh0FcV9XoyTqfT0bFjR7/nFIRI4Vh5wwA393AZtdbwOtyJdNRGYb44A6moD+zFVf4f3qgHTeIOJAiC1yS6lwOFWRDg3lY90oIAVRjsUKC8pvkH9MIKtRzIv0xAmTkwntPQ0NEoGJ977969MRqdv3cHDx5s8hqHw8FvvznLGvr0qU9xq18fOnSo2ffPy3NaI3br1k0TCfszpyBECsdczkDn9HI2w7PYHfx0uCyUS4o6jpQ592xfnIFUAtUwzGyxYXZly91dh4KFBAGCEGXEmwyo5dHhFgSomQBDG3RCDDSqRSg0bxOqKAqFrpOgTF+FwW6e0y0FG96gioJjjXoSfBC+tYbJZOLMM88EYNOmTU1e8/PPP2sOPeecc472/aFDhwLwyy+/UFFR0eTYDRs2NLjW3zkFIVI46vKv79s1hazOzpI2KQkKLFomwJ8gQDWO8LMcyH28lAMJguA1er2OBJPzQS/cyoE0e9AEU9A7IQYaNRMAzZ/2lJqtWO3O03t/y4Famsdb3O1BfbEt9YSxY8cC8N5771FS0vgh5cUXXwRg5MiRDboPDxkyhMzMTGw2G8uXL280bseOHVq33+uvvz4gcwpCpHDMlVnskhrH0JOdHbV/kH4BAaPWatcc3wKRCfBXy+XuPiflQIIg+ITWNdgSrkFAZJUCAcQaDZreormHc9UZCHwXBqcltB5seEsw7UFVrr32Wnr16kV5eTnTpk1j9+7dAJSXl/Pwww+zbt06jEYjf/nLXxqMMxgM3H777QAsWbKEpUuXakLfjRs3MmvWLBRF4YILLmDQoEEBmVMQIoVjrkxAl5Q4hvR2BgE/5pVidQn9Bf9QRcHgZxDgoYV0a7hnbX2xK/UWsQgVhCgkKdZIQWUdVWHmDhSp9qAq6YkmqupszdqEHnc5Axn1Op9deGKNBpJjjVTW2YKSCQgWMTExLFy4kClTprBr1y6uuOIKkpOTqa6uxuFwPrD8/e9/Z/DgwY3GXnPNNeTm5vLOO+/w1FNPsXDhQkwmk9aQKCsriyeffDKgcwpCuGO1O7Tywq6pcZyc6XTEMlvs7DxSweAeaaFcXlSglgIZ9Do6+5i9hfpMQJnZitXuwGTw7YzdvVtwsLK27kgmQBCiEC0TEGblQMURHgS01jCswJW675gU61e5U33X4MBmAoKdgTn99NP58MMPmTRpEt26daO2tpakpCRGjhzJq6++yvjx45scp9PpePTRR3nmmWcYOnQocXFxWK1WevfuzcyZM3nzzTdJTk4O6JyCEO4UVNahuLwBuqTG0TU1np4Zzm60PxwsDuHKogc1COiSEofRxwd3aFi/X9qMZswTVE1AW5QCgWQCBCEqUXsFhFsQoG6OERsEtNIwzN9uwdo8iTH8VmL262biTiAyAY8//nijmvym6NSpEw8//DAPP/yw13OMHTtWq/P3Bn/mFIRwRXUGAmcQADD05Ax+KzHzw8FSZowM1cqih3zNGcj3HgHQ8KG9uMriszucemDTFs5AIJkAQYhK1Nr1cBMGq6cckRoEtHZCX+hnjwCVjABnAiI9+BKE9ojaIyAlzkhCjHNPV8XBWw6VtNivRPCMI1qjMN/1AABp8SbU5K8/DkFaJiCxbTIBEgQIQhQSruVAkf4wqqZ8m7MIVYXBmT6eAqkEOggoifAyLEFojxx1ZQK6ptY/oP6PKwgor7Gyt6AyJOuKJgIVBOj1uoB0ey/SGoVJJkAQBB+pDwLCSxgc6Q+jrWUCVGFw5wCUA7U0j7dE+ucuCO0RzRkotf5QoWdGgra/SL8A/8kPUBAAgekVoHYcboseASBBgCBEJeFYDuRwKJSag+9SE0w6tPJwrmYCfK0HVQlkEGC1O7SmY5H6uQtCe+Soy2igq1sQoNPpGHpyBwA2SxDgFw6HwlGXJqCbn5oACEyvgJLqthUGSxAgCFFIYkz4lQNV1Fqxu2pYI7FPANSvuylhsKIoFFQESBOQELggoMxc33VYggBBiByOuzIBJ/Yc0ZqGHSxBUUQX4CtF1XVYXP0WuqUl+P1+/h7eKIripgmQTIAgCD6iugOFUybAfWOM1IdR9aSn2mKn1tqw1Kqi1kadzXlD8dcdKJAWoe7vEanBlyC0R46WN84EQL0uoLCyjkPF5jZfV7RwpKy+uaO/7kDgtIaGeq9/b6mss2lBiWgCBEHwGbUcyGwJH02Au91lpAYB7g/RJ9p3Frp1Cw5UOVCN1U6Nn/+GDYMAUwtXCpGKze7gQGGVnApHEQ6HwvGKxpoAgD6ZSVpncekX4DuqKDg5zkhynP97o3p6r9b1e4u7lkDKgQRB8JlwdAdSN7iEGANxpuC3Qw8G7sHLieIvtRRIp4OOfp7iuM/TnBORp6hBQGq8ya9mOEL4Mv+T3Yx6aj0rNuWFeilCgCiutmBzlU+eGATo9TqG9HZmA0QX4Dv5pc4goFsARMEAGUn+ZXBL3LQEUg4kCILPhKMwONLtQQFS4kwYXGbQJ2YCjrsyAR0SY/1+2Hb/jJprTOYpahDRVjcVoW1RFIX3c48AsOmAnApHC6ozEEDXlMYPqf/jpgsQfCOQzkDgvzuQWkaU2IYHZRIECEIUomYC6mwObK4aw1BTHAU2lXq9TiupOfG0J1CiYHA2BzK6gg1/dQFqEJEewZ+70Dy7jlZqTeoOFkl9uL9U19n44pfjWEO8b6o9AuJNBlLijY1eV8XBv5fWaGUtgnfU9wjwXw8A9RngyjobdTbvyzg1UXAblQKBBAGCEJWowmAIn14BpVEQBEDzDhAFardgP0XB4LQBDJQ4WHoERDdf7y3Qvs4rrhZdgJ889MEObn5tK099tjek6zjmZg+q0+kavd6vawqJMc59fsshyQb4wpFytRzIf2cgOKGM04d9Wy0HaitRMEgQIAhRiVoOBFBlCY+SIC0TEOEONc3ZhGpBQAAyARA4m9CSKPnchaZZv6dQ+9pssWu/h4L3VNfZWPvzUQDe/fFwSLOox5qxB1UxGvScLboAv1DdgQKVCXA/wfelJKioje1BQYIAQYhKEt2CgHARB0dLWUp9Q5gTy4Favml7S3pi02VH3lISJZ+70JjKWis/5pU2+N7BouoQrSby+WLXcWqtzgf/oioL3+4vCtlajjVjD+qO6AJ8p8Zi1/bGQAmDU+KMmAzOrE2RDw5B6j1F1Ra0BRIECEIU0iATECZBQEmEdwtW0TIB5uBmAtQbQaDcgUQYHH18v78Ym0PBZNBpf1eHJAjwmQ9/Otrgv9/PyQ/RSup7BJzoDOSOqgvYX1Dl00NneybfTUcRKGGwTqfzq2GYai0q5UCCIPhFrFGPS1caNpkAtd4x0oOADM0LuulMQKafPQJUtEyAj04TKpIJiF7W73WWAg3pnUF252QADhZLEOAL5TVWvnF9nsP7dADg053HQnaI4q4JaI6B3VOJMTof47aKLsArVFGwQa8L2MEN+OcQpB3YiDBYEAR/0Ol0YdcroLQ6OjIB6vrdMwHVdTaqXU29AiEMds7jfyZAURSxCI1SFEVh/R6nKPj8rEx6d0wE4GChBAG+8Pkvx7HYHcQa9Tw5fhAxRj21Vgef7jjW5mtRFKVVTQBArNHAmT3SANEFeIsaBHRJiQto/xT1FL+o2vvMjGgCBEEIGPW9AkLvDlRns2snatESBLine93FmIETBjszAf70CTBb7FhszhpnyQREF/sLqjjielA8PzuTU1xBwCHJBPjER9udvRYu6tuJrqnx/KFvZwDez237kqCKGhs1Vue+3TW15VKVoaIL8Ak1CAiUHkBFfYD3NoPrcCjawZKUAwmC4DfhlAlQswAQPUFAqdmKw9XRUy0FX61nWAAAIABJREFUAsgMVBDgSgn7Iwx2HyuZgOhCLQXqkhJHdudkLROQV2zWfi8FzyittvDdPqcIeNzAkwC48sxuAHy/v4jjbn/fbcHRivp69ZY0AVAfBPxytIKKWmuL1wr15AfYGUhFLeU50TiiNcprrNhdf7ciDBYEwW8Sw6hrcLFbajTSrSpVYbDdoVBZ6/xsj7syAekJJmKNgen0mOEmQPb1oc49CJBMQHTxtcsa9PysTHQ6HSd3dHqd19kcHG3jh9ZI55Odx7A5FBJiDFyY3Qlwfq7pCSYcCqxxdWRuK1RRsMmgazV4P6tnOga9DkWBHw+VtnitUE9+mbOxXqBEwSr1mjHvyoHc75GSCRAEwW+SXA3DwikToNdBarwpxKvxD/cNWt241UxApwCJgqFeGOxQnKdEvqAGATFGvdZYSIh8zBabVv5xfnYmAD0yEjQzAHEI8g61FOgP/ToT7/o7iTHqtazAqjZ2CTpeXr+f6PWNG4W5kxhr5IxuqYDoAryhvkdAYIOAjs1YSLdGkVv5UHobHpRJECAIUUpiTPiUA6ni1PSEmFZvauGO+wat1nAWBrBbsIp7SthXcbB7o7Cmuo4KkcmmA8VY7A4Meh3D+3QEnCJR9YFGegV4TmFlHRt/LQbqS4FU1JKgXUcr2H2sos3WdNSDHgHu1PcLKA7amqIJh0PhaHmwNAG+uQOp16fGmzTHp7ZAggBBiFLCSRhc4kqNRkNJSpzJoJ2qqxu3KgwOlB4A6jMB4LsuQOxBoxO1FOisnmkNMmsnq+JgCQI85uMdR3EokBxnZGRWxwavndUzjV4dnGVW7+e0XUnQMQ96BLgz1NU5ePvv5dRYQr/fhztFVXVY7c4Sy4CXA7kyATVWO2aL5wdwqoV2W5YCgQQBghC1hJMwOFoahamkn2ATWlAZ+HKgWKNBC+R8DgLEHjQqUUXB52dlNvh+7w6R5RC080g5k1/ezJe7jodsDR+5GoSN7t+lkZ5Hp9Nx5WBnNuCD3Pw2E1yrPQK6eNh9fEjvDHQ6sDkUcg6LLqA1GjYKC6wwuKNbBtebbEAo7EFBggBBiFq0IMCL04hgoTUKi3BRsIom/nI9nBdUOH++zgEsB4L6bICvNqGlkgmIOg4WVZNX7BQ1XuASsaqomYADEZIJePaLfXy3v4hZ/9nGlhA0uzpaXsOWPOe84wZ2bfIatSToaHktm9qo3MbbTEBqgklrFidWoa2j6gFS4owkxwVWo9ZQM+b5vq3qy9rSGQgkCBCEqEUVBoeDO5DWKKyNU53BQrMJdW3yx4MgDHbO45vdnIo6TjIB0YPaIKxjUgz9uqY0eE0NAg6XmLHZHW2+Nm+orrNpHXotdgczXtva5mVM/91+FEVxunqp2ooTObljIoNdDbnebyOBsFqv3lqPAHfO7pUOwI788qCsKZoIljMQQEKMgVhXTX+JFw3D6rsFSyZAEIQAEE7lQMXRlglIqM8E1FrtVLisQgMpDHbOE6BMQJR87kJ9KdDI0zIbiezVXgFWu6KddoYr6/cWUmdzYDLoyEiModRsZeq/t/jVHM9bPtruLAUac0ZXTC10jb36LGc24OOfj1FrDW7Nvdli0/YTTzMBUP9AW+SlILU9ov5tBFoUDM4Sso6uXgHe/FtIOZAgCAGlPggIvVBMzQRES1mKeyagMAjdglXSm+hO7A2aO1CUZGDaO7VWOxsPOEtSVGtQd7qnx2NwBQYHw1wX8MmOYwCcd2pHXrrxHGKMeg4WVXPrih+pswV/zzpcYib3cBkAlzVTCqRy6YCuGPU6KutsfBFk/YJaCgTeBQEdtBJF7/zp2yOqJiAYmQCoP833RhOg9hVQm421FRIECEKUkhRGzcKiTaCqPZybrZooGAJfDqS1oPfVItRcbxEqRD4/HCyh1upAp4P/d1rjIMBk0NMj3flgE84OQXU2O+t2O8uaxpzRhbN7pfP0hEEA/HCohHvf3Y6iBFeEq2YBOibF8j+ndGjx2g5JsZoIe/W24JYEqUGATufdoYLWqVYyAa1yJMhBQIZ2eON5QFYs5UCCIAQSNRPgjU1ZMFAUJeoEqh3cNnlVFJwcZ9QaDQUKfzIBNruDMrOagYnsBm2CE7UUaFD3tGadttSSoHDuFbBhfzFVdTZ0OmeDLnB69N8zJhuA93OP8OwX+4K6BrVB2KUDumjZk5a4ylUStH5vodfdYL1B7RGQmRTbYonSiagPj2aLd9aU7RE1COiWHqRMgJe9Atz3ahEGC4IQEFRhsNWutEl6vTkqam3YXNZ60ZYJKK22uomCA795d/AjCChz6zLc1jcWITh87RIFn2gN6k4k2ISqpUBDemVo9dMAs84/levO6QHAgi/38d6Pvwdl/oNF1ew84mz+NW7QSa1c7eTivp1JjjVicyj89+ejQVkXuNmDelEKBL5bU7Y3zBYbpa4H7m4BtgdVUbsGF3m4b7tneiUTIAhCQFAzARBaXYD7A2y0ZALUU9iqOhu/lzpPlQJdCgT1gl5fxJKlDT53yQREOodLzPxa6Hywb0oPoBLuDcNsdgefu+rqR5/RpcFrOp2OR686gxEup565q7az6UDgbTk/+smZBeiSEsfZPdM9GhNnMnDJAOd6VwWxJEizB/WwR4CKr9aU7Y0jDXoEhEc5kHvQJsJgQRACQmKMexAQuvSwexAQLbXp7qUYe45XAoF3BoL6G3u1xe61K4n7g4C4A0U+3+xzlgKlJZgY1D2t2es0m9DSGqxhaBO65VCptieM7t+50esmg54XJp/FaZ2SsNoVbl3xI78WVgV0DR+6SoHGDezayGGpJdSeAbmHy4JWbqWWA3X1MhOQEGMgzuR8pAtmuVKkk+9yBjLodUE5uAHv9Rnq34NeB2ltvFdLECAIUUqSWyYglOJgdYOLNxkCXjMfKtyDmd3HXEFAEMqB3B/eS70UB6uZgJQ4o1e1xUJ48vUeZxDw/07LbLGGXQ0C7A6FwyXmNlmbN3y601kKNKBbKt3TE5q8JiXOxPKbhtAxKZbyGitTX9kSsAfbvccr2XvcGVR4WgqkMuzkDtrDebB6BhyrcJ5Ud/GiRwA4syje1qK3R9RMQJeUOI+0IL7g7g7kicC9yPW7nZ4QE7Q1NYfcGQQhSmlYDhS6IEB9GG1OyBiJpMabUPdq1SI0GKdKHfyo8613mxA9QKRjsTnYsL8IaFkPAM4ShxhX0BduugBFUbQgYMwJpUAn0iMjgZennEOcSc9vJWZuX5kbkDWopUA9MuIZ1D3Vq7F6vY4rBjuzAe/n5gfFwai+W7D3f7cdtFp0yQQ0hyYKDlIpENSX9FjsDo8O4NS9va31ACBBgCBELTFGPSaD80k1lJmA4igMAvR6XaMSm2CUAyXHGbWTIV8zAekJogeIdH7MK6Xa4iwHG5nVdGdbFYNeR48M5wPOwaLwygRs/71cK3cZ3b/lIABgcI80nrjWaR367b4i9rlK73xFURQ+dFmDjht4Ejqd96euV7lKgvKKzWz7rcyv9ZyIxebQmkZ1SfH+IVUzEpBMQLPkB9kZCBoevHhyeKP2dgiFgYMEAYIQxYRDwzD14TVaRMEqJwY1wcgEOIMN50O8tw5B0Rh8tVe+3ut0Bep/UopHv2fhKg7+xJUF6NMpiT6dkjwaM25AV3pmOMuG3t562K/5dx6p0Gr5x7XSIKw5srsk07drCgCrcwLrXqQ6jYH3mgBwq0UXYXAjqutsLFq3j09dzlS+fL6e4i7u9aR5W0mIegSABAGCENWo4uBwEAZHiz2oyolBTTAyAeDuNOFlJsAsQUC0sN6lB2itFEgl2DahBRW1HPBSrKsoimYNOsaDLICKXq9jwjndAacrj8Xmu9hZbRB2SsdE+rke5H3hisFOLcFXuwt9fo+mOFbhW7dgFa0cSITBGrVWO8u/O8jIJ77iyc/2Um2xkxxnbLUczR/iTAYSXfo3TzIBavYnFPdICQIEIYoJh67BJVpZSnQ9jJ7odBQMYTDUf27eBgElUdagrb1yrLxWE59fkN3JozHBbBhWa7Vz+aLv+eMz33hl37mvoEpbj7cPYNec3R29znnCrXYa9hab3cGaXKeYd9wg30qBVM471dlhOL+spoHlpL+oeoC0BBNxJu9NFDqKMFjDZnewcstvjHrya+Z99AvF1RbiTHpuPf8Uvr3nQga24LAVCLzJyqii91DotyQIEIQoJtHVMCwsMgEhSHUGkwy3nyfeZGjgxhRI1M/N1yAg2jIwzbF27VomTZrEWWedxeDBgxk3bhwvvPACtbW1rQ8OY75xdQlOjjVyZk/PHlzUcqD8spqANwr8clcBxypqsTkU5r633WPrWjUL0C0tnv4neXcK3zU1npGuLIivJUH//fkoR8pr0engalddv6/065pCguukd2teqV/v5Y6vPQJUNFeadiwMdjgUPvzpCH985hvufe9njpTXYtTruGFYL9bffSH3XdK3TWw46x2CWv63UBRF08mEImsrQYAgRDGqJqAqhG3k20MmoHNKrF8niy2hNQzzWRgcXZ97U8yfP5877riDrVu3YrFY0Ol07Nu3jwULFjB+/HjKygIr4GwrLDYHa3c4S1hGnNbRY6tXNQhQFPitOLDi4NVu1piHis08+8U+j8ZppUBndPHpb0XtJPz1ngLtYdlTFEVh6TcHABjdr4uWKfEVo0GvBWRbD5X49V7u+NojQMXdnz4YzkXhTEm1hfdz8hn33Hfc9mYOB4qqnQHfWd1YN+cC/nHlGXT2MbjyBfXwpbVMwLrdBdq/u6dBfiCRIEAQopik2NBrAuotQqPLpcb91CZYTWfA7WbiRYpfURQ3i9DoDgLWrFnD8uXLMRgMPPTQQ2zbto2cnByWLVtGZmYme/fuZe7cuaFeplfUWu28tvEQF/zrK60/wIUelgKB8yQ51ui8vQeyJKik2sLXe5zlOIN6OB9YXvr2ADvyy1sc91uxmV+OVgDelwKpXNS3MxmJMTgUeG+bd4Lcjb8Ws/OIc/4Z55/i0/wnck6vDMDZ/CxQ1PcI8DEIcO0VNodCRU3o9vy2oM5mZ8OvRcz/ZDfjnvuWsx/9nNtX5mq/Z6P7d+bT20fy9ITB9OzQdD+KYOJJzwZFUVj01X4A/t9pHel/kneWtYFAgoBWKC0t5dxzz2XMmDGhXoogeE2o3YHqbHYqXQFIRgjsz4KJexCQGSRRMNTX9HuTCaix2qlzCSijORNgt9tZtGgRALfccguTJ08mJsb5844YMYLFixej0+n46v+zd+fxUdR3H8A/e+c+SEIIAQnhCAhyCAmnHGo98EFFrYpCEZRDEaS0Wo/aVh9stYc+IsqhoIKi1kqRekAVFYFwS0SuICHhCEnInU02m73m+WN2JhtybbJXsvN5v159NWZnMkNYZuc7v+/x7bf44YcfAnmqbqmps2HN9zkY/9K3+MOnx3Cx0gyNWoXpGT1xx9Xup7Co1SqfFAd/fuQibA4BEQYt1s/JQN+uEbA7BPzukyOwtTCdWJoNEB9hwNVXxLbr2HqtWm7P+fHB82160r3auQqQnhLb7uNfLj1FDAKyC6tQZbZ65WfWpwO1r32la8AfbLMCBEHAz0VGrN2Vi9lv78ew577CfW/uw8rvcnA0vwqCAITo1Lh+YCI+XTgOq2eORP/EyICdrzupWXvOlOKws83swsl9/XJel/NNEmuQsFqtePLJJ1FWVoboaP9HaESeCnRhcIWp/sMxuFcCfBcEtKc7kOvTp2DuDpSZmYmzZ89CrVZj1qxZjV6/6qqrMH78eOzcuRObN2/G1VdfHYCzbF1lrRXrM/Owdneu/G9Gp1HhlyN7YsGEPu16kpkSH4bsIqNXZwVIqUA3De6G6FAdXrpzCO5alYljF6vw5s5cPDypT5P7Sa1BbxiU6NFE1HvSe2LtrlzklZqwP7cMo1LjWt3nZGEVdjjrKuZNaPr82mPYFTHQqFWwOwQcPlfhduemlhR6mA7k+m+9rMaCPp6fUsCZrXb869AFrPn+DM41MQF7UPcoXNMvAdf0i8eIXrHtKqj2hS5urOC+8W0OAGBkr1iM6t3FL+d1OQYBzTCbzXjiiSfw3XffBfpUiNot0IXBrjeuwbwS4Mt0oC7ySoAVDocAtRs3Ua6rBsEcBOzbtw8AMGDAAHTp0vSH6NixY7Fz507s2rXLn6fmltLqOqzbnYv1mWflFTODVo37Rl2BeRNSkRTd/oFGKV6eFXC2tEYejiU9kR/RKxazxqTgncw8/N/Xp3DT4G5yPYLkUpUZh5zFs21pDdqU/omRGNYzBlnnK/DRwfNuBQFSLUCfhHBcN8D9lKrWRBi0uDIpCj/lV+JgXpnHQYDdIaDIOX28velABq0GkSFaGM22VgtSO7paix0b95/Dmu9zUFRV/2dJjDLIN/3j+sYjvoNORI9vpTtQ1vkK7HJOAV84ua/PaspawyCgCTk5OVi6dClOnjwZ6FMh8kh4gGsCpCBApQKiQ4NrJcC19WaiL9OBnOk8doeAKrPVrc4W0gePTqPyWdeijuD0aTGfNjW1+TzvlJQUAEB+fj5MJhPCwvyfH9yUmjobbvy/nXJP93C9BjPHpODB8b2R4IWVpd5eTgfafPgiALHeYLTLzffjN6bhq+NFyK+oxZOfHMEHc0c3CFS3HS8CAESFaBvs1153j+yJrPMV+OKnAvzp1kGICmn+ulJQWYstWeJ5z70m1a0Aui1GpsTip/xKHPBCcXBJdR3sDjHFqb1BACDefBrNNrn3fGdjNFuxYe9ZrN2ZK1/HtGoV7ry6Bx4Yl4IB3SIDdsPcFq5d3Zp6ePO6sxbgyqQoTEoL3JINawJc2Gw2LFu2DLfddhtOnjyJuLg4TJ48OdCnRdRugU4Hki7iMaE6j9IAOqK4BkGADwuDIxou8buj3GVacGf4wGyvS5fEItXExMRmt+natf7pb3Gxd4c7eeJofqUcACy+rh92P3ktnrx5gFcCAKB+JaCg0oxai2c1QYIgYLOzx/5tw7o3+LccbtDihWmDAQD7csvw4YGGLTylCa3XDUyEXuv5LcfUoUkI0alhtjrw2Y8FLW779u482BwC4iMMuN3DtqBNkYqDs85XeDTEDECDjkeeBAHtaSTQEVSYLHjlq1MY9+I3+OvWbJTWWKDXqvGrMb2w44nJeOmuIRiYFNVprmfSCqz08MZVdqERXzmD40CuAgAMAhowmUzYsGEDrFYrbrjhBnz66acYNGhQoE+LqN3qJwYHpjD4knMCpi/TZQIlRKfBo5P74sZBiXKRoC+4Fva6WxwcrG1ZL1dTIz7lDg1tPm3G9bXq6rZNufWl7CJxAFiP2FAs/UV/r/cuT3VJyzlb5tlqQNb5CrnLUFM305PSusopQn/54oR8Q1thssgDxW70MBVIEhmiw5SrkgAAH7UwM6DKbMXGfecAAA+M7eWTXPGRKWKRsdnqwLGLLXdIao3UJjJcr0GkB6t3nWlWgCAIOFVkxF++PIFxL36DV7f/jCqzDaE6DeZe0xu7npiM528bjOSY9qfFBYprmtLlqzJvfOdcwUwI9+nkYncE7zpxO6hUKmRkZGDhwoUYPXp0oE+HyGOBTgeSu1148GSrI/vtjWk+P4Y0gr7GYnf76V6wDmi7nM0mvq91uuZTQqRuQYDYTag97HY7srOz27SPySQWMTa3317n5NseEao2/2x3CIKAUK0KtTYBu378GarKiHb/rLf3iisovWP1UFcVILuq8RP46QP0+Oa4GpV1Nvz6/b3447Xd8HWOETaHAINGhSRVObKzPbtRloxNBDYB+PF8BbbtPYKU2MarJx//VI7qOhtCtCqMirf65HcMAEmRWhQYbfjiQDbCatvfeejIz2K9RWyoGqdOnWr3z9HYxDajuQWlPvszA62/v5sjCAJOl9Zh19ka7DpbjQuV9U/Jw3Rq3DYwGtMGxSA6RIPygrMob3mxp8Oy2uu7V2WdOA17uRjIXKyy4j8/iilq09LCcfrn9v9du8Nut0OjaT4AZhDgIjIyEhs2bAj0aRB5jTwnwGKDIAh+X3YsrPJsAiaJYsP1qLHUur0SIG0X7CsBISHi+8pqbb5Fo8VS/ztrKVjwt9wy8bx6N3ED6w0qlQrdo3TIKbPgYlX7W1jaHAJ2nBFXLa7t03zLxegQDR4enYAXdxRhz7ka7Myrwe6z4urByB5hCPFCKpBkcGIIukfqcNFoxbafqzA/o2FOtdUuYPNx8ab6pn5RiDL4rmPMoK6hKDAacazIjLsGt//nFNeIAW18mGe3ZTEh4p+10txx5gQ4BAEni83YlSfe+BdVNzy3xAgtbuwXhdsGRiPCh39X/qTTqBChV6Pa4kCFuf7hw8c/lcMhAF3DtS3+e/KXoAsCqqqqUFVV5fb2BoMBCQn+LcrwxVMloqaUFos34Q4BOHLsJEJ0/s0AzC0Uu4JobdUtvnf5/m5ZmEbMN87Ou4jsqNpWtz9bKBYqqiwmr/5OW3uq5G/h4WLKi9nc/ATZ2tr631dERPuehms0GqSltW3VR/q9N7WfIAg4X5UHABg7KAVpad3bdV6tGZBcg5yyAlSrQtt8/pJvThahsi4HKhXw0C+GttixqH9/AfuLDuKbk5ew+mAZjM4b0V+O7oe0NO/m5N8/Vou/bcvGd3m1eHF6vwb1Bp8cuoASkx0atQq/mXo1enbxXTH4dZWh+DrnJ5wstaJ///7tftBiPWwCUIE+SXHt/rsCgLTSPODHcpgcWo9+Tmtaen9LLpSb8Ob3Z7D1WGGDDj+AmK520+BuuHlwEgYnd55c/7boGlWA6pIaGKLjkZbWC4WVZnydI3arWnhdGgYNTPH5OWRlZbX4etAFAe+++648PMYdGRkZfPpPQSvU5aa/1ubwexBQYvLO0y2lizZIT/fcS2epdNaASE8Fg1W3bt3w448/ygXCTSkqEgvwVCoV4uPj/XVqLbpQXisX6w/o5rungSnx4s1vngezAv7t7Ao0JjWu1ZalKpUKy24fjBte+V7Og9ZpVJjsxdackrtG9MA//puNshoLvj5RJNcJCIKAN3eKN1pTrkryaQAAiAPIADEF70xJDfoktC/QLPBwRoCkvj99YGsCdv1cgkc/+KHBrJgB3SLlG//+iRFBeePvKi5CjzMlNfLfxZs7z8BidyA+Qo970nsG+OxE/GQOAG8/VSJqTmRFLQCxOK5bjxS5Y4g/CIKA8lrxw3hY/xSkpTV/I8D3d8t6ZtXiQL4JgiHcrd9R7WdiIm2/XklIS0vx2nm09lTJ3/r27Ytt27YhLy+v2W3Onj0LAEhOTm6xgNifsgvF9Bq9Ru3Tf5PS1ODcdrYJNZqt+K9z0Je73XW6x4Tidzel4dlPjwEAxvaJ90l74MSoEExO64rtJy/hnwfPy0HAjlPFOOn8/c6f0HzrWG/pkxCB2DAdyk1WHMwra3cQIKVOJnoYBEh1QBW1VtjsDmg1/n3wIwgC1u7KxZ+/OAGHIHYrevCa3rh5cFKjGRLBznXQY1mNRS5Uf3B8aocZahZ0QcCiRYuwaNGiQJ8GUYcQ7tJlwt9tQstqLLDYxTQWX7bQVAJ5YFg7WoQGs4yMDLz++us4fvw4qqqqEBUV1WibzMxMeduOQuoM1KdrBHQ+vEmTbrqKjXWorrO1eWbE1qOFqLM5YNCq29TF5P5RvfDl0UJk5pT69InnL0f2xPaTl/D9qWIUVNYiKTpUHg42tk8cBidH++zYEpVKhRG9uuDrE0U4kFeOe9KvaPPPEAShflqwh9dKqSuNIIgDBr3VctYdtRY7ntp0BJudsxkGJ0dh9cyRnbK7jzfESQPDqi14e3cuaq12RIVoMWN0298jvsIWoURBLFxf/7TB3x2CCrzU95rqB5OVmVov8LQ7BFTUitt1CfLC4PT0dCQkJMBms2HdunWNXj969Kg8Kfjee+/19+k1S3pS7ctUIAANVhnaMzlYmg1w/ZWJLQ7lupxarcI7szOw/TcT5Sf0vnDdwK6Ij9DDIQD/OngBR/MrkZkjtiSd64dVAImUEiRNRm6rCpMVdc45A55eK13nl/izTeiFchPuWpUpBwDThifjXwvGKjYAAIB4599FXmkN3snMAwA8MDYFkW34t+RrDAKIgphWo4bBWTBXY/FvEFDkXN7Wa9WIDes4F73OKE5eVm79Q73CZIHg7E7XJchbhGo0GixZsgQAsHr1aqxZswZ1deLvaM+ePXj44YchCAImTZqEoUOHBvJUG8guFJtXpPk4CIgL1yMyRHz639bJwYWVZvmGetqwthf16rXqdqfGuEunUeOOq3sAAD4+dAGrduQAANISIzGpv/8afox0zgnJLalBsbHtN96uD0w8rQmICdNDmuXmr4Fhe3JKceuK3Th2sQoatQrP/s+VePnuoR0m5SVQpJXYYxerYHTOP3hgXO8An1VDDAKIglz91GD/DgxzbQ8a7AVgvhYrpwO1vhLg2kY02FcCAODOO+/EL3/5SzgcDvzjH//AiBEjMHz4cDzwwAO4dOkS+vfvj7///e+BPk2ZxebAmWLxhtzXQYBKpZJTgnKL2xYEbPkxH4IAxIbpMMGPN9RtdfdIMQg4V2bCZ0fEWpi5E1L9es0ZnBwlP2w5dLaszfsXVokdrPQatccpfBq1Sv4ZJT4uDhYEAe/szsWMtftQVmNBTJgO6+dk4MHxvXnNR306kOT+UVd0uBTNoKsJIKKGwg1alNZY/J4OJA8KYz2Ax6QPjuo6G+psdhi0zT9hc336F9vBPnB8QaVSYdmyZRg7diw++OADnDhxAmazGSkpKbjpppswd+7cdrcG9YWc4mrYHOJSja/TgQCxOPjIhco2Fwdv+kFMBZo6tHuD9psdTd+ukbj6ihj8cE6cC9AtKgS3DvVNy9XmGLQaDO0Zg/25ZTiQV46bBrctBaqwUrxZT4w2eOXmOS7cgJJqS7tXAvJKanCioAp6rVr8n0YNnfP/Dc7vXaq2Yv3hMnx1WkxtG5gUhTUzR/i8G1PVEUUqAAAgAElEQVRn4jqsUa9R46Fr/Jei5i4GAURBLlBTg6UgwNNuF9SwwLe8xopu0c0HAdJKQGSI1qdFpx3NlClTMGXKlECfRqtOOlOBokN1fgmQpbqAttQEnCiokusW3O0KFEh3j+wpBwGzx6UEJGhJT4nF/twyHMxrx0pApbgSkBTlnfz5uAg9UNS+moAqsxX/89quNjWS+J8hSfjrXUMQpuctpau48PqVgDtH9OiQtXHK+YQgUihpAqO/uwNJ6UCe5rhSw7Se1j7YSxXSGaizkm6u07pF+iVlorc0K6DU/VkBUkFwr7gwDO8Z45Pz8qb/GdodqfHhSI0Px/RRgem8ItUFHL1YBVMb668KvPzApH5WQNtXAnIuVcufFaE6DTTq5t+jahXw5M0D8Nr04QwAmtCzSyi6hOsRrtfg4Yl9An06TeLfGlGQC9RKgFQYzPagnosO1UGtEic/t1YXoJT2oJ1Vtp86A0mkWQFlNRZU1lpb7dnvcAj41Dkg7PZhyZ0itzvCoMXXSydCAFq8afWlq6+IhUoldufKOleBsX3dH0zn7QcmUpvQUjdbCrvKrxBXJaJDdfjxjzcAEP9MVrsDdTYHLDYHrHYHTv58GpEGDdKHdMyb244gTK/F54vHw2YXOmyaFFcCWrFo0SJkZ2dj69atgT4VonYJD1BhcAFrArxGrVYhNkxqE9ryB3tZjTLag3ZW2S4rAf7Qu41tQvfmlso3pZ0hFUiiVqsCFgAA4k1zWqL4d3ogr22tQr1dPxXnwdTgC+ViENAjtj41SaNWIUSnQXSoDgmRBnSPCUVylB5RBmV3/3FHUnRohw0AAAYBREEvQu//lQCTxQajWTxet2j/DasJZvL0yVY+2KU2olwJ6HgqTVY5OPbXSkBMmF5u0etOm9DNh8VUoOFXxChuwqunRjrnBRxsY4cgeVCYl1YC4jxYCbhQLqaNKbm/v5IwCCAKcoFIBypsMCiMHybe4O7AMOl1BgEdjzQpGAD6J/onCADqi4NzW1kJMJqt+PKnQgDisCdqm3RnXcAPZ8thc05Lb43RbIXReW32Vk2A1JWmPTUB+fJKQMd9ek3ewyCAKMgFojBYSidQqYCufhxbH8zcHRjGlYCOSxoSlhwT6tepob3jWg8CiqrMuHfNXhjrbNBpVLjFh5N+g5VUHFxjscsF4K2RaqcAb9YE1LcUNlvblgbaVDoQBS8GAURBTl4J8OPEYGklIC7coKg2lb7k7sAw6XUlzAjobE76uShY0lqb0OxCI6a9Xj/x9YVpVzUadEStS44JRXfnjby7rUKlGQFqFZDgpd+5a2vKtqQECYIgBwHJDAIUgZ/OREGuPh3If4XBbA/qfVKhb+stQusabE8dh9wZKCkwQUBuSQ0EQWjwWubpEty1MhMXK80I12vw1qyRuHtkT7+eXzCRVgMOnHWvOLjAOSMgIdIArZcemLgOqWpLcXC5yYpa58oBVwKUgS1CiYJchNwdyH8rAUWVbA/qbVJ6z6miavxpy7EmtxEEAWarmIvcJYJBQEciCIJLZ6Aovx5bSgeqMttQbrLK76VPDl3Ak5uOwGoX0DXSgLdnp2NQ92i/nluwSU+JxZYfL+JgXhkEQWi1xarcGciLtVMRBi30WjUsNkeb6gKkomCANQFKwSCAKMgFpDC4SvpgY0qBt0jTJstqLHgnM6/V7RmAdSz5FbVyAaj/04Hqb+hyS2oQG6bD8u2n8crXpwAAaYmRWDc7nR1hvEBaCSiqqsOF8tpW20PKq6Ze/PeqUqkQF65HQaUZJW1YCZBSgSIN2lbnSVBwYBBAFOTCnYXBJosdDocAtR96aXu77zUB1w7oigfGpuCic5hPS9JTuvCGroORVgF0GpXfW29GhugQH6FHSbUFpy8Z8cH+c/jXoQsAgPF94/HGjKsR5cdC5WDWPzESkSFaGM02HMgraz0IkFcCvHutjIsQg4CyNtQE5LMeQHEYBBAFOSkdCBCLg/3RlaR+JYAfJt4SotPgT7cOCvRpUDtJRcF9EiICUizfOz4cJdUWPPef4zBZxLzvO6/ugb/ccRX0WpYHeotGrcKIXrH4LrsYB/LKccfVPVrcvsBXQUB422cFSOlATAVSDv7LJwpy4a5BgB+Kg212B4qN4hI0VwKIRNkB6gwkSXHWBUgBwJLr++HvvxzCAMAHpHkBrXUIumQ0yzfe3m6iIBUHtycdiEXBysGVAKIg57oS4I/i4JJqCxzOBiSsCSASBaooWJLmDD60ahVevHMI7hrR8hNqar+RvcTJwT9fqkZ5jaVRu94zxdV4c+cZfHIoHxbnULHW0obaKl6aGtyGwuD8CgYBSsMggCjIua4EmPwwK0BqeQewOJUIACw2B3KKqwEEbiXgnvSeqDLbMLF/PEb06hKQc1CKoT1joNOoYLULOHS2HNdfmQgAyDpfgVXf5WDb8UJInVpjw3SYOyEVw3rEePUcpOGCrbUUlrjOCGAQoBwMAoiCXJhOI3/tj5UAaQJmhEHr16moRB3VmZJq2JzLY/6eESCJDNFh6S/6B+TYShOi02BwcjQOn6vAgbwyaDQqrPouB/ty69ODesSGYu41qbh7ZE+E6jUt/LT2iWvjSkBlrVX+fEiOYU2AUjAIIApyarUKYXoNTBa7X2oCCuUZAUwFIgLqU4GiQrSsk1GI9JQuOHyuAmt2nsHq78/I378yKQoLJvXBlMHdvDYcrClSTUBptcWteQXSKgDAlQAlYRBApADhBq0zCPD9SkBhlbMomNOCiQAAJwqkouCoVm/GKDiM7BWLNYCc9jO+bzzmT0zF+L7xfnkPxDu7A1nsDhjrbK22gJWCgHC9BjFhXMFVCgYBRAoQYdCi2Fjnl3SgQmdNAOsBiETZhVUA6otzKfhNHtAVd4/sAZtDwOyxvXFVD/9OYnadGF5WbXEjCBC7FCXHhjJQVRAGAUQKIA0M889KgHMCJlcCiAC4dgZiEKAUOo0af71raMCOH+fSkai0pg4prQyoq+8MxHoAJWGDYCIFCNeL8b4/goCiKs4IIJJU1lpx0VknE6jOQKQ8ITqN3B66xI3iYHYGUiYGAUQKIH0YVPu4MFgQBJfCYAYBRKeKjPLX/RkEkB+5Fge3RgoCkmMYBCgJgwAiBZBmBfh6JaCq1oZaqxhoJEXzw4TopDMVKDkmtNW8bCJvkmcFuDE1ON9ZE8B0IGVhEECkAFIQUO3jYWFSPQAAJHJaMJFcFMxUIPI3eVZATcsrAZW1VlSZxc8GpgMpC4MAIgWI8FNhsBQEaNUquUUdkZKxKJgCJd6ZDlTSykpAvsuMgGQGAYrCIIBIAfyVDlTkrAfoGmmAWs02c6RsgiDI6UAMAsjf4sLdmxosdQYK0akbdBWi4McggEgB/FUYXOAMAjgojAgorrHB6EyzGNAtKsBnQ0rTRaoJqGl5JeCCSz0AZwQoC4MAIgXw10qAlA7EIIAIyC0Xn8DqNCqkJrTcp53I26TuQGWt1ASwM5ByMQggUgC/pQNVsT0okSTPGQT0SYiATsOPW/KveGdhcFmNBXaH0Ox2+ZwRoFi8KhEpgFQYXO3jIEBOB2IQQITccjENg/UAFAjSSoBDACpMza8GXKhge1ClYhBApADSxOA6mwM2u8NnxyliOhCRTFoJYBBAgRDn0qGtpTahcjoQVwIUh0EAkQJI6UAAUOOj4uA6m13OPeVKACmdzSHgfKX472Egi4IpAGLDdJDqfJtrE1pdZ0OFyQqA6UBKxCCASAEiXIIAXw0Mu1RV/yHDlQBSuguVFtici25cCaBA0GrUiA2TpgY3vRLgOiOAQYDyMAggUoCGKwG+CQKkegCAhcFEUmegyBAtkhgUU4BIff9Lm1kJkNqDGrRqJERwwKPSMAggUoBwZ2Ew4LviYKk9aGyYDiE6TStbEwU3KQgY0C2SvdcpYKTi4OZqAlzbg/J9qjwMAogUIFSngTTA11crAdK0YK4CEAG5ZewMRIEnTw1uJgiQpgWzKFiZGAQQKYBKpZI7BPmqMJiDwojq1XcGYlEwBY68EtBKOhDbgyoTgwAihfD1wLBC50oA859J6arMVlyqEf+dDeBKAAWQvBLQTGHwBQ4KUzQGAUQKIdUF1PioO1AhpwUTAQBOFRrlr/snMgigwGmtJoDTgpVN2/omRBQMpDahPisM5rTgoFVeXo4pU6YgJCQEr7zySovbGo1GrF69Gl999RXy8/MRHh6OwYMHY+bMmZg0aVKL++7evRvr1q3DTz/9hNraWnTv3h3XXXcd5s2bh5iYGJ8c0xesdgEA0CtGj+hQnd+PTySJdwYBTc0JMFlscnDAIECZGAQQKYQv04EcDgGXjM6VAKYDBRWr1Yonn3wSZWVl6N69e4vblpeXY/r06cjNzQUAREREwGg0YteuXdi1axfmz5+PpUuXNrnv+++/j+effx4AoNVqERISgry8PKxduxaff/453nvvPfTs2dOrx/SV0ald8Nx1SbgiRu/X4xJdLs7Z9tNotqHOZodBW9+5zXVGQHIMawKUiOlARApRHwR4vzC4tMYiP/1kTUDwMJvN+M1vfoPvvvvOre2XLl2K3NxcpKSk4OOPP8ahQ4dw4MABLFy4EACwevVqbNu2rdF+Bw4cwLJlywAADz/8MPbv349Dhw7h448/Ru/evVFYWIiFCxfC4XB47Zi+pFKpMPqKcHSP4ioABZY0JwCAPNFdcsHZGUinUaFrJGcEKBGDACKF8GU6UFFV/aAwpgMFh5ycHNxzzz1u30Dv378fmZmZ0Gg0eOONNzBkyBAAQHh4OBYvXoyZM2cCAF5++WUIgtBg3+XLl8PhcGDq1KlYsmQJwsPDAQBDhgzB22+/jbCwMGRnZ+Pzzz/32jGJlCDOZQDY5cXBrjMC1GrOCFAiBgFECiEXBvsgCJDqAQxaNXOgOzmbzYZly5bhtttuw8mTJxEXF4fJkye3ut+HH34IAJg4cSL69OnT6PX58+dDpVIhLy8PP/74o/z9M2fOYP/+/QCAOXPmNNovKSkJt956KwBg8+bNXjkmkVJEhWih04g3+JfXBUjtQTkjQLkYBBApRLgPVwJcZwRw6mTnZjKZsGHDBlitVtxwww349NNPMWjQoFb327dvHwBgzJgxTb6ekJCAfv36AQB27tzZaL+YmBgMHDiwyX3Hjh0rb2uxWBrt29ZjEimFSqVCF2dK0OXpQHJnINYDKBaDACKFiND7rjCYnYGCh0qlQkZGBt5991289tprSEhIaHWfyspKlJSUAABSU1Ob3S4lJQUA8PPPP8vfy8nJAQD07t272QBS2s9qteLs2bMeH5NISZqbFcAZAcTuQEQK4cvCYE4LDh6RkZHYsGFDm/YpKiqSv+7WrVuz23Xt2hUAUFxcLH/v0qVLAIDExMRW95P27devn0fHJFISaVZASc3l6UDOmgAGAYrFIIBIIfxRGMyVgI6hqqoKVVVVbm9vMBjceuLfnOrqavnrkJDm3wPSa67bS1+HhjZ/I+L6mrS9J8dsK7vdjuzs7DbtYzKJ+dZt3Y/I2/QO8eb/TH4xsrPFBJA6m0OuEXAYS5CdXdOmn8n3d+dgt9uh0WiafZ1BAJFCyCsBPpgYLKUDcVpwx/Duu+9ixYoVbm+fkZHR5qf/ruz2+tUlna75wnC9Xt9oe5vN5vZ+rtt7ckwiJYkOEW8CK831/wYu1dR/DiRG8FZQqfg3T6QQ/ugOxBkBymQw1LchtFqtzW4nFfW63rRLT+rd2Q+ov6n35JhtpdFokJaW1qZ9pCekbd2PyNv6FWiBYxUwQye/HwtPFQM4B61ahbHDB0HTxhahfH93DllZWS2+ziCASCGkdCCrXWg0OdITNXU2GJ2BBacFdwyLFi3CokWL/HY8qa8/ANTV1TW7ndksBosRERGN9pVea0ptbf1kU2l7T45JpCRSTYBrYbDUGSgpJqTNAQAFD3YHIlIIKR0I8G5xcCEHhSlet27d5M4+UqFvU6RiXtdCX6mo1539XPf15JhEShIvFQZX18lD86QZAWwPqmwMAogUIqJBEOC9lKAiZyqQWgUkcPS8IoWHhyMpKQkAkJub2+x2UntP18Feffv2bfBaS/vpdDpcccUVHh+TSEmkFqF1NgdqLOIDIHYGIoBBAJFiuK4EeLNDUIEzCIiPMECn4SVFqTIyMgAAe/fubfL14uJiuVe/tK3r1yUlJc328s/MzAQADB8+vEFuf3uPSaQk0rAwAChzpgTlV3BGADEIIFKMMH19DYA3VwI4I4AAYMqUKQCA7du3ywPAXK1evRqCIKBv375IT0+Xv9+zZ08MGTIEALBmzZpG+xUUFGDLli0AgOnTp3vlmERKItUEAPWzAuR0oFimAykZgwAihTBo1dA6C8C8uRIgzQhge1BlmzhxItLT02Gz2TB37lwcPHgQgFjUu2LFCrkF6aJFixpNBl66dCkAYMuWLfjLX/4Co9EIADh69CgefPBBmEwmpKWl4aabbvLaMYmUIkyvlR8ClVZbUGezo6hKDAaSY7gSoGTsDkSkECqVCuEGLSprrd4tDGZ7UHL661//ipkzZ+LChQu4//77ER4ejrq6Orm3/yOPPNLoRh4AxowZg8WLF2P58uV45513sGHDBoSGhsoDvrp27YpVq1ZBrW783Kq9xyRSkrgIPUxltSitrkNBRX0zB6YDKRtXAogURCoO9kU6EFcCqHv37ti8eTPmz5+P3r17w2q1wmAwICMjA8uXL8djjz3W7L4LFy7EunXrMGHCBERGRqKurg7JycmYMWMG/v3vf6N79+5ePyaRUkjFwaU1FrkoWKNW8eGNwnElgEhB5IFhXpwaLK0EsD1o8JLmDrQ2eAYAIiMjsXTpUjnFpy3GjRuHcePGtXk/T45JpASubUKleoBuUSHQspmDojEIIFKQcC+vBFjtDhRXi7mlLAwmIuqY5JWAagvyDWwPSiIGAUQKIqUDVXupJqDYWAfn7BkGAUREHZQ8NbimTp4QzHoAYhBApCDheu+uBHBaMBFRxyfNCiittsBicwAAerAzkOIxCCBSEG+nA0nTgiMN2gbDyIiIqOOIj6gvDK6qtQLgjABiEECkKBHOwmBvzQmQOwMxFYiIqMOS0oHKaiwQnDmcTAciBgFECiKvBHipOxBnBBARdXxSYbDdIcjfY2EwsTcUkYKEe7kwmDMCiIg6PqlFqESlApKiGQQoHYMAIgXx9rAwzgggIur4YsMbBgHdokKg1/IWUOn4DiBSEK8XBjtXAtgelIio49Jp1IgJ08n/nczOQAQGAUSK4s3CYEEQUMCVACKiTiHOZTWARcEEMAggUhTXlQCpQ0R7VdZaUefsN82VACKiji3O2SYUYHtQEjEIIFIQKQhwCIDZ6vDoZ7kOCmNhMBFRx8aVALocgwAiBYlwGejlaUqQVBSs06gafLgQEVHHE+fSIYjtQQlgEECkKK5TfT0tDpaCgK6RIVCrVR79LCIi8i1pVgDAdCAScVgYkYKE6zXy13PXH0Soy3+3VbGxDgDrAYiIOgPXWQHdY3jdJgYBRIoSYdAiOlSHylorfr5U7ZWf2a9rhFd+DhER+U7vePFanZoQDoO2/Q+AKHgwCCBSEK1GjfcfGoXdp0u88vNCdBpMHdrdKz+LiIh8Z1zfOLx+39VI6xYZ6FOhDoJBAJHCDE6OxuDk6ECfBhER+ZFKpcItQ5ICfRrUgbAwmIiIiIhIYRgEEBEREREpDIMAIiIiIiKFYRBARERERKQwDAKIiIiIiBSGQQARERERkcKoBEEQAn0SSnLo0CEAgEbTtkEddru9XfsRdQZ8f3cO0t/TiBEjAnwm/sNrNlFjfH93Dq1dszknoJPgPzQKZnx/U7Dhe5qCGd/fwYErAURERERECsOaACIiIiIihWEQQERERESkMAwCiIiIiIgUhkEAEREREZHCMAggIiIiIlIYBgFERERERArDIICIiIiISGEYBBARERERKQyDACIiIiIihWEQQERERESkMAwCiIiIiIgURhvoE6DmffHFF3j//fdx4sQJOBwO9OjRA1OmTMGcOXMQEhIS6NMjapXRaMSGDRvw9ddfIy8vDxaLBV27dsWoUaMwe/Zs9O/fv8n9cnNzsXLlSuzZswfl5eWIjY3F6NGjMW/ePPTr18/Pfwoi9/G6TZ0dr9vKoRIEQQj0SVBjL730EtatWwcA0Ol00Ol0MJlMAID+/ftjw4YNiImJCeQpErUoLy8Pc+bMQX5+PgDAYDBArVajtrYWgPi+fuGFF3Dbbbc12O/IkSOYNWsWTCYTVCoVIiIiYDQa5Z/x8ssv4/rrr/fvH4bIDbxuU2fH67ayMB2oA9qyZQvWrVsHjUaDZ599Fj/88AMOHz6MtWvXIiEhAadOncKTTz4Z6NMkapbNZsMjjzyC/Px89OjRA+vWrUNWVhYOHz6MLVu2ICMjA1arFc888wyOHTsm71dZWYn58+fDZDJh8uTJ2LFjBw4ePIjvv/8e1157Lerq6vD444/LH1BEHQWv29TZ8bqtPAwCOhi73Y4VK1YAAObOnYsZM2ZAr9cDAMaPH4+VK1dCpVLh22+/xQ8//BDIUyVq1tatW5GTkwONRoMVK1Zg3LhxUKvVUKlUSEtLw1tvvYXU1FRYrVasXr1a3m/Dhg0oKytDz549sXz5ciQmJgIAEhMTsXz5cgwaNAgmkwmrVq0K1B+NqBFetykY8LqtPAwCOpjMzEycPXsWarUas2bNavT6VVddhfHjxwMANm/e7O/TI3LLjh07AACjRo3CwIEDG71uMBjk5eQDBw4AAARBwIcffggADW6iJDqdDg899BAAMe/aYrH47PyJ2oLXbQoGvG4rD4OADmbfvn0AgAEDBqBLly5NbjN27FgAwK5du/x2XkRtceWVV+LGG2/EhAkTmt0mISEBAFBdXQ0AOHPmDIqLiwEAo0ePbnKfMWPGyPscPnzYm6dM1G68blMw4HVbedgdqIM5ffo0ACA1NbXZbVJSUgAA+fn5MJlMCAsL88epEblt9uzZmD17dovbSGkR3bp1AwDk5OQAAFQqFXr37t3kPrGxsYiJiUFFRQVOnz6NUaNGefGsidqH120KBrxuKw9XAjqYS5cuAYCcU9eUrl27yl9LETg1b9OmTUhLS8NVV10V6FMhp/Pnz+Ozzz4DAEycOBFA/Xs/NjYWBoOh2X2l9/+mTZswadIkDB48GGPHjsXf/vY3H581UdN43SYl8MZ1W9qeOgauBHQwNTU1AIDQ0NBmt3F9TVqSI+os6urqsHTpUpjNZhgMBsyZMwdA/Xu5pfc+ALnX+tGjRwEAer0edXV1iI6O9uFZEzWP120Kdt66bkv/VqhjYBDQwdhsNgBiMU1zXAtv7Ha7z8+JyFssFgsWLVqEI0eOAAB+//vfo3v37gDce+8DgEajkb++++678ac//anB94j8jddtCmbeuG5L739pe+oYmA7UwUjRstVqbXYb1+r61v7hEXUUNTU1mD9/vtyBYvbs2bj77rvl19157wOQh9YAwJQpUxgAUMDxuk3BylvXben9z/d+x8KVgA4mPDwcAGA2m5vdxvUmKCIiwufnROSpS5cuYf78+Th+/DgAYNasWY0GJ7nz3gfEZWlJa0vQRP7A6zYFI29et6XX+d7vWBgEdDDdunXDjz/+2GLxTFFREQCxGj8+Pt5fpxaU9uzZg02bNuHw4cMoLS2F3W5HbGwshg8fjvvuuw8ZGRmN9qmrq8N7772HrVu34vTp07BarYiLi8OwYcNw7733yu3QXFVVVeHtt9/G9u3bcfbsWQBAfHw8Ro4ciZkzZ2Lw4MFNnl95eTk2bNiA7du349y5cxAEAd27d8fEiRMxZ84cuV2bNxUWFuKDDz7AN998gwsXLgAAevfujalTp+L+++9v1AcaAAoKCvDOO+/g+++/R35+PjQaDVJSUnDdddfhmmuuwZIlS3Dx4kUAwKJFi/Doo4/K+27btg0ff/yx3DqutLQUixcvxsyZM5Geni5vd+HCBVx33XUNjnvPPfcAADIyMrBhwwbv/iKI3MTrNgWb06dPY+7cuc1etyVJSUkAxM8qq9Xa7JN+6f3vWiBPgccgoIPp27cvtm3bhry8vGa3kW4ik5OT+SS0nRwOB5555hls2rRJ/l5UVBTMZjMKCwvx5Zdf4ssvv8Tzzz8v32gC4pLmrFmz5BtWrVaL0NBQFBYWYuvWrdi6dSsee+wxPPLII/I+5eXluPfee+W/U51OB4PBgAsXLuDChQvYsmUL/vKXv+D2229vcI5ZWVlYuHAhSkpKAIjLrmq1Gjk5OcjJycG//vUvLF++vMmgo712796NX//616isrAQAhIWFwWq14tixYzh27Bi2b9+OdevWNQgEvvnmG/z2t7+VC77CwsJgt9tx/PhxHD9+HCtWrIAgCNBoNPjDH/6Ae++9FwDkUfLbtm0DIN4cSbZt24Zt27bhwQcfxBNPPAFArAXo0qULysrK5O1iYmKg1WpZFEwBxes2BZOsrCzMmzcPlZWVja7bl+vTpw8A8TP13Llz8n+7Ki8vlz9T+vbt67sTpzZjTUAHIz15Pn78OKqqqprcJjMzs8G21HaffPIJNm3aBI1Gg2effRZZWVk4cOAAjhw5grffflvu6f23v/2tQS7ve++9h8OHDyM6OhpvvvkmfvrpJxw8eBCZmZm49dZbAQCvv/46zp8/L++zYsUK5OXlITk5GR999BF++uknHDp0CNu3b8f48ePhcDiwbNmyBl0TCgsLMX/+fJSUlCA1NRXr169HVlYWDh8+jH/+858YNGgQqqqq8Mgjj+DMmTNe+Z0UFRVh8eLFqKysxNChQ/HJJ5/g8OHDyMrKwt/+9jcYDAYcOHAA//jHP+R9jh8/jiVLlqCmpgbDhw+XV1WysrLw3HPPQa1WQxAEAMALL7zQ4INk2bJl2LZtG3r06IFXX30VWVlZSE5OBgBMnjwZWq0Wa9euxfvvvw9AfOqa9SsAACAASURBVOL07LPPNjjn1atXY/fu3VixYoVXfgdE7cHrNgWLkydPygGAwWDA8uXLmw0AAKBnz57ydXvv3r1NbrNnzx4A4oOsIUOGeP+kqd0YBHQw6enpSEhIgM1mw7p16xq9fvToUXniZEv/MKllH3zwAQBxzPmMGTPkJ3M6nQ5jx47F008/DQAwGo0NbrKli9ztt9+OCRMmQK0W/wnFxcXhxRdfREJCAiIjI+UcStd9Zs2ahWHDhslPvHv06IFXXnkFer0eISEh+Pnnn+V9Vq5ciYqKCsTFxeG9997DqFGj5P2GDh2K9evXo2fPnjCZTHjllVe88jt5/fXXUV1djR49euCdd96RU5S0Wi1uvfVW/Pa3vwUg9ueXAqOXX34ZdXV16NOnD9555x0MGjQIgJj/vHbtWjgcDvnnZ2dny1+fOnUK//znPxEREYGNGzfipptuQkhICG6++WYAQG5uLhYvXgwAeO2112A2m2G1WvHWW2955c9K5E28blMwqKmpwaJFi1BZWYmQkBCsWbMG119/fav7Sdft9evXN6oNcL1uT5s2rcVZAuR/DAI6GI1GgyVLlgAQn3KuWbNGLoTcs2cPHn74YQiCgEmTJmHo0KGBPNVO7YUXXsCrr74q9zq+XL9+/eSvXXt6S0VQu3fvbjTwR6PR4JtvvsHevXtx4403Ntrn66+/btQfPCoqCocOHcKuXbswbNgwAIAgCHKKzMyZMxEXF9fo/CIiIjBv3jwAYjqOp72XBUHA9u3b5WM2Nc30jjvuwIIFC/DUU0/BYrHAaDTKTzcXLFggd4kAgFWrVuHcuXMAgEmTJgEA/vOf/8ivS19PnDixwYClBx98ENHR0cjLy8POnTuhVqtRXl6O7du3Y8mSJTh27FiD4xB1BLxuUzBwvW7/8Y9/xOjRo93az/W6vWDBAnklvKioSL5uh4WF4aGHHvLZuVP7qARprZ46DEEQ8Oyzz+Ljjz8GID6d1ul0MJlMAID+/ftj48aNiIyMDORpdhqbNm3CU089Bb1ej59++qnJbex2Oy5evIi8vDycPHkSe/fulZ/cbdiwQV7C37Fjh3zzbTAYMHr0aFxzzTUYP358syPTN27ciOeeew6AePM+btw4eR+pqMrV+fPn5acvH374IYYPH97kz3Xd7v3338fIkSPd/ZU0UlBQIN+sf/zxx24t2e7ZswcPPPAAAGDnzp1ywZfFYsGYMWNQXV0NlUqFyMhIOUUiNjYWGo0GlZWVcks5lUrVINCxWCzNplTodDo8//zzeOqppwAAH330kRw8EQUSr9vUmV1+3W7q4dPldu/e3eDrhx9+WA5+o6KiYDQaIQgCdDod3njjDUyYMMFn50/tw8LgDkilUmHZsmUYO3YsPvjgA5w4cQJmsxkpKSm46aabMHfuXLbZ8oJLly5hw4YN+Pbbb5GXl9egz7GU5gMArnHyxIkT8Yc//AF///vfYTKZsGPHDrl/cq9evXDzzTdj5syZDbp/3Hfffbh06RLeeustVFdXy0WvADBgwADccsstuO++++S/U9fC15Y6Kbg+QS8vL2/vrwEA5OJjAG53LpGOqVarG3QpOnXqlLziIQhCgxv6ps5TEIQGx29KaGgoJkyYgAULFiAqKsqt8yPyJ163qTO7/Lrd2jX5cuPGjcOWLVuwatUqZGZmoqysDLGxsUhPT8eCBQtw5ZVX+uK0yUMMAjqwKVOmYMqUKYE+jaC0Y8cOLF68WM5fTExMRL9+/dC3b18MGTIE/fr1w9SpU5vc9/7778fUqVPx3//+F99++y327dsHo9GIs2fPYtWqVdi4cSPWr1+PgQMHyvssWbIE999/P7Zt24Zvv/0Whw4dQm1tLU6ePImTJ0/igw8+wMaNG5GUlAR3F+dc8+1dO+u0R3smmDZ3noMHD26Q/5+TkyO/jzdv3oyBAwfixhtvRF5eXrNt51oitS0l6oh43abO6PLrdnukpKTgxRdf9NIZkT+wJoAUx2Qy4YknnoDZbMbVV1+Nbdu24fvvv8fatWvx1FNP4ZZbbml1qmFUVBTuuusuvP7669i3bx8++ugjzJkzBzqdDlVVVfjzn//caJ+EhATMmDEDa9euxf79+/Huu+/innvugUqlwsWLF7F8+XIAQJcuXeR9pN7KTXF9LTY2tq2/hgZiYmLkr0tLS5vdLjc3V36yL52nw+Fwqz+66z7S+RYUFLT/pImIiKjdGASQ4uzfvx8VFRUAgP/7v/+T24G6ysnJkb+WnnjbbDZ8+OGHeOGFFxo8MdFoNBg2bBh+97vfyfMBjh49CkDsLvTee+/hueeea1BIrNfrMXr0aDz//PO48847AUCuV+jZs6d8U95cyzWgvu2aWq1G//792/ZLuMwVV1wh5ypnZWU1uY3FYsG0adOQnp6OrVu3YtCgQfIKhDvnGRsbK6c3SZ2H9uzZ02BFw1VOTg4mTJiAe++9F0eOHGnfH4yIiIiaxCCAFEcq1AOaTmmxWCxYuXKl/N82mw2A2Cpz9erVWL9+vVz81xwpb12r1eKvf/0rNm7ciM8++8ytfVQqFW666SYA4lyCpp7MV1dXy23Xxo4d63GxoVqtxi9+8QsAYpGxVNzl6tNPP0VtbS30ej1GjRqFqKgojB8/HoDYEaWpsfGFhYX46KOPAAA33HCDHDTccsstAID8/Hxs3LixyXN69dVXUVRUhDNnzngc5BAREVFDDAJIca666iq58PeZZ56RU1Lsdjv27NmD6dOny0/yATS4uZ0+fToA8eZ8zZo1MBqNAMReyF988QXefPNNAJDrCUJDQ+VJwK+88gr++c9/yj/PbDbj/fffx7///e8G+wBiy82YmBiUlpZixowZ2L9/vxywHDlyBL/61a9w/vx5hIaGyjMNPLVgwQKEhYUhNzcXc+fOxenTpwGIQdGmTZvkFKcZM2bI6Ty//vWvodfrkZOTgwceeECejyAIAjIzMzFz5kxUVlYiISEBjz32mHys4cOHy21U//znP2PlypVyUVpxcTGeeeYZuXj68vajRERE5Dm2CKWg11SL0JdeeqnBUJ/IyEjU1tbKT/1HjRqFixcv4vz583j66acxa9YsAOLN/rx58+T++ID4BL+6ulpOa8nIyMCbb74p37hWVVVh5syZOHnyJADIbTOl9mmA+GT873//e4OuRIcOHcLChQvljjqhoaFQqVTySkZMTAz+8Y9/yE/jvWHnzp1YvHixfIzIyEiYTCa5cPjaa6/Fq6++Cr1eL+/z1Vdf4fHHH0dtbS0AcS6Cw+GQ/zs5ORmvvfaaPEhMUl1djccee0xuxapWqxEeHi4HVgBwzz334Pnnn2+w34ULF3DdddcBYItQIiKi9tL86U9/+lOgT4LIl06cOIHt27dDo9HIOfvjx49HcnIySkpKYDQaUVdXh5iYGAwfPhyLFi3C7373O5w/fx5HjhxBXV0d7rjjDgBi/v/UqVPRtWtXmEwm1NTUoLq6GlFRURg6dCgefvhhPP300w0Kiw0GA6ZNm4bIyEh5+9raWnTp0gXp6en4zW9+g4ULFzbq8NO9e3fccccdcrFxVVUV1Go1UlNTMX36dPz5z39u0IHIG3r16oXbbrsNdrsdlZWVqKiogF6vx1VXXYVFixbht7/9LbTahk3F+vTpg6lTp8LhcMjnaTAY0L9/fzzwwANYtmwZevTo0ehYer0eU6dORWpqKkwmE4xGI2pqahAVFYWRI0fi8ccfb3K4TFVVFdavXw8A+OUvf4lu3bp59XdARESkBFwJICIiIiJSGNYEEBEREREpDIMAIiIiIiKF4cRgoiBQUFCAu+66q937796924tnQ0RERB0dgwCiIGC321FSUhLo0yAiIqJOgoXBfnbo0CEAYpcZIqLORGoVO2LEiACfCREReYo1AZ2E3W6XP4CJgg3f30RERP7lt3Qgo9GIDRs24Ouvv0ZeXh4sFgu6du2KUaNGYfbs2ejfv3+T++Xm5mLlypXYs2cPysvLERsbi9GjR2PevHno169fs8dzOBz46KOP8Mknn+D06dNQqVTo3bs3pk2bhvvuu6/FJ/HtPaY7pOO2dcBRdnY2ACAtLc2j4xN1RHx/dw5ZWVmBPgUiIvISv6QD5eXlYc6cOcjPzwcgDk9Sq9XyRFGdTocXXngBt912W4P9jhw5glmzZsFkMkGlUiEiIkKeJmowGPDyyy/j+uuvb3Q8QRDw61//Gl9++SUAyJNbzWYzAHEa7FtvvdVg6qmnx3SX9CHKIICoHt/fnUN7r19ERNTx+DwdyGaz4ZFHHkF+fj569OiBdevWISsrC4cPH8aWLVuQkZEBq9WKZ555BseOHZP3q6ysxPz582EymTB58mTs2LEDBw8exPfff49rr70WdXV1ePzxx+XAwtXKlSvx5ZdfIiwsDK+88gp++OEHHD58GC+//DLCw8Oxb98+vPTSS4328+SYRERERESdhc+DgK1btyInJwcajQYrVqzAuHHjoFaroVKpkJaWhrfeegupqamwWq1YvXq1vN+GDRtQVlaGnj17Yvny5UhMTAQAJCYmYvny5Rg0aBBMJhNWrVrV4HjV1dVYt24dAODpp5/GlClToNFooFarccstt+DFF18EAHz44YeNbubbe0wiIiIios7E50HAjh07AIgpOAMHDmz0usFgkNOADhw4AEBM5/nwww8BADNmzGiUtqPT6fDQQw8BAL744gtYLBb5tc8++wxGoxExMTGYNm1ao+PdcMMNSE1Nhc1mw+effy5/35NjEhERERF1Jj4PAq688krceOONmDBhQrPbJCQkABCf4gPAmTNnUFxcDAAYPXp0k/uMGTNG3ufw4cPy9/ft2wcASE9Ph1bbdN3z2LFjAQA7d+6Uv+fJMYmIiIiIOhOfBwGzZ8/G8uXLMXv27Ga3+eGHHwAA3bp1AwDk5OQAgNzRpymxsbGIiYkBAJw+fVr+vvR1ampqs8dLSUkBAPz888/y9zw5JhERERFRZxLwOQHnz5/HZ599BgCYOHEiAODSpUsAxJtug8HQ7L5du3ZtsL3r11I+f0v7lZeXw2q1enxMIqLOxmJzBPoUiIgogAIaBNTV1WHp0qUwm80wGAyYM2cOgPq0oNDQ0Bb3l1p/1tTUyN+Tvm5pX2k/1+09OSYRkScEQcC5UhP8NcB96UdZGLHsK+SW8DpGRKRUfhsWdjmLxYJFixbhyJEjAIDf//736N69OwCxrSggFuO2RCrelbZ3d1/Xol9pSqknx2wru90u90V3l8lkAoA270fUGSj9/f3p8Qq8sa8E9w6JxewRcT4/3tajF2GyCvjuh5Ow9Ipwez+73d7ioEUiIuo8ArISUFNTg/nz58udg2bPno27775bfl162i6l6jRH6tDjeuPuzr6unX2kfT05JhFRewmCgE9PVAIAskvMPj9enc0Bk1VccYgO4Q09EZFS+X0l4NKlS5g/fz6OHz8OAJg1axaefPLJBtuEh4cDqJ/w2xzp9YiI+idZ4eHhqK2tbXFf6TWVSoWwsDCPj9lWGo2mzZNROVGVgpmS399Z5yuQXyU2JqgTdD7/HZwvMwE4AwAYMagfesWFu72vNDGYiIg6P7+uBJw+fRr33HOPHAAsWrQITz/9dKPtkpKSADQs3G1KUVERgPpiXaC+w1BLhbvSfnFxcXIbUU+OSUTUXpsP1w8tLDf5fv5IaU39MeIjmm+CQEREwc1vQUBWVhbuu+8+XLx4ERqNBs899xweffTRJrft06cPAMDhcODcuXNNblNeXo7KSnEJvW/fvvL3pa/z8vKaPZezZ882OI6nxyQiag+r3YH//HhR/u+yGovPi4NLjHUAgFCdBuGGgJWFERFRgPklCDh58iTmzZuHyspKGAwGLF++HPfee2+z2/fs2RPJyckAgL179za5zZ49ewCIufxDhgyRv5+RkQEAOHjwoFz0e7nMzMwG23p6TCKi9tj1c0mDJ/N1NgdqrU1ft7ylpFoMAuIj9a1sSUREwcznQUBNTQ0WLVqEyspKhISEYM2aNbj++utb3e/mm28GAKxfv75Rnr7VasVbb70FAJg2bVqDvv7XX389DAYDiouLsWnTpkY/97///S/OnDkDnU6Hu+66yyvHJCJqj387U4F6xNa3Ji43tdycwFNyEMBUICIiRfN5ELBq1So5veaPf/wjRo8e7dZ+Dz74IKKjo5GXl4cFCxbg/PnzAMSc/CVLluDYsWMICwvDQw891GC/6OhoPPjggwCA//3f/8W//vUv2Gw2OBwOfPnll3jqqacAAHfffbdcP+DpMYmI2qq6zob/Hi8EADwwNkX+fnmNb+sCSqrFnx8XziCAiEjJVIIPE1AtFgvGjBmD6upqqFQqxMW13v969+7dDb5++OGHUVcnPrmKioqC0WiEIAjQ6XR44403MGHChCaP++ijj8otSA0GA9RqNWprawEAo0ePxptvvtlgXoCnx3SX1F1j2LBhbdpPyd1TKPgp8f39yaEL+M3HP0KvVePAM9djxP9+BZtDwPo5GZjQP8Fnx1248Qd8fqQA0zN64i93tC2tsb3XLyIi6nh8WhV26tQpeRKvIAgoKSlp0/7jxo3Dli1bsGrVKmRmZqKsrAyxsbFIT0/HggULcOWVVza5n16vx6pVq/DRRx9h06ZNOH36NGw2G9LS0nDrrbfiV7/6VZMBgCfHJCJqi81ZYirQ9QO7IjpUh5gwPUqq63zeIaiU6UBERAQfBwGDBw/2eAJoSkoKXnzxxTbvp1arMX36dEyfPt1vxyQickdRlRm7T4sPRW4fJjYk6BKuQ0l1Hcr8lA7EIICISNkCMjGYiEjJ/vPjRTgEICZMh0lp4syR2DBxddL3NQFcCSAiIgYBRER+J3UFuuWqJOi14mW4S7gYBJT5MB3Ianegwtl9KC6CLUKJiJSMQQARkR+dKjLi2MUqAMC04cny92PDpZUA37UILa3mtGAiIhIxCCAi8qPNLrMBRvSKlb/fRUoH8uFKgJQKBAAJDAKIiBSNQQARkZ84HAI+zboIQFwFUKlU8mvSSoAvC4OlIECvUSMq1Kd9IYiIqINjEEBE5CcH8sqQXyHOK7ltWHKD17qE6wD4eiXAOSgsQt8gACEiIuVhEEBE5CfSbIAhPaLRt2tEg9diwuprAnw1w1FaCWBRMBERMQggIvIDs9WOz44UAKifDeBKqgmw2B2osdh9cg4lRrYHJSIiEYMAIiI/+C77EoxmGzRqFaYO7d7odalFKOC7WQGlNRwURkREIgYBRER+IM0GGN83HgmRjW/CY12CAF8VB3NQGBERSRgEEBH5WIXJgm9PFgNoOBvAVbheA71GvCT7amBYsZwOxJoAIiKlYxBARORjX/xUCIvdgTC9BjcMSmxyG5VKhVhnh6AKHwUBUncgrgQQERGDACIiH5MGhN04qBvC9M33548Nk2YFeH9qsN0hoKyG6UBERCRiEEBE5EPny0zYn1cGALi9mVQgiVQc7IvC4HKTBQ5n59H4SKYDEREpHYMAIiIf2vKjOCE4PsKAcX3iWtxWXgnwQTpQaXX9z+RKABERMQggIvKhr08UAQCmDk2CVtPyJVeqCfDFSoDUGUitqg82iIhIuRgEEBH50LlSEwBgaI+YVrftItcE+C4I6BJugEat8vrPJyKizoVBABGRj5gsNnlAV4/Y0Fa3l2YFlPsgHYjtQYmIyBWDACIiH8kvr5W/7hEb1ur2UmGwL7oDsT0oERG5YhBAROQjF5xBgE6jQtcmpgRfTsrVrzBZIAiCV8+ltJorAUREVI9BABGRj1yoEIOA7jGhULuRhy+tBNgcAox1Nq+eS0k1ZwQQEVE9BgFERD5yoVwsCnanHgCorwkAvN8hSE4HcmNFgoiIgh+DACIiH5HSgXrEtF4PAACxYTr5a293CJJWAuLCmQ5EREQMAoiIfEYOAtxcCQjVaWDQipdlb3YIEgRBHhbGlQAiIgIYBBAR+Uy+Mx0o2c0gQKVS+aRDUFWtDRa7AwCQwJoAIiICgwAiIp8wW+1yHr477UElUocgb9YElNTUyV+zMJiIiAAGAUREPnGhwYwA91YCAJdZAV5MByox1gcBXVgTQEREYBBAROQTUmcgrVqFxKgQt/eTpwZ7cyXAuSIRHaqDXsvLPhERMQggIvIJaSWge0woNG7MCJB0cXYI8mZhcAkHhRER0WUYBBAR+UC+c1BYcoz7qUCA60qA9wqDOSiMiIguxyCAiMgH2toeVCIVBnu1JoDtQYmI6DIMAoiIfKB+WrD7nYEAX9UEiCsBbA9KREQSBgFERD7Q3pWALlKLUJMFDofglXPhtGAiIrocgwAiIi8zW+0odrbldHdQmCQ2XCwMdghAldk7dQFyTQDTgYiIyIlBABGRl12saN+MAKBhH/8yL6UElRidNQFMByIiIicGAUREXialAmnUKnRrw4wAoL4wGPBOm1CTxYZaqx0AW4QSEVE9BgFERF4mBQFJ0SHQatp2mQ3RaRCm1wDwTptQaRUA4EoAERHVYxBARORl+RViZ6C2zgiQeLNNaLGzHgBgEEBERPUYBBAReVl9Z6C2tQeVSMXB3mgTKhUFh+s1CHWuMBARETEIICLysva2B5V4cyWAnYGIiKgpDAKIiLysflBY+4KALl4cGMbOQERE1BQGAUREXlRns6OoSnz63u50IGklwAuFwaU1zpUAdgYiIiIXDAKIiLyooMIsf+3xSoA304G4EkBERC4YBBAReZFUD6BWAd2i2zYjQBLrg3SgOAYBRETkgkEAEZEXSfUASdGh0LVxRoCkiw8KgxOYDkRERC4YBBAReZG0EpDczlQgoL5FaGWtFXaH4NH5FDMdiIiImsAggIjIi/IrnO1B2zkoDKgvDBYEMRBorzqbHUazDQBbhBIRUUMMAoiIvMjT9qBAfWEwAJR5UBdQWl2/L1cCiIjIFYMAIiIv8nRaMADEhOnkrz3pECTVAwBAHGsCiIjIBYMAIiIvsdgcKKwSW4R6shJg0GoQYdAC8GwlQAoC9Fo1Ip0/j4iICGAQQETkNYWVZgjOOl5PCoOB+uJgT9qESu1BEyIMUKlUHp3P/7d35/FR1ffewD9n1myTfSMLJAFCWAIIsiOiVkXqbW2VSpVbhApIW65Ln/bahfq017baW+vFxw2rua1cXK7e6kVFa3FhMYBgwh5CAiRA9n2bTGY7zx9nzslkmWTWZIZ83q+Xr1ecOSfnBwzkfM/vuxAR0dWFQQARkZ/I9QCCILUI9YU/2oT2dgZiKhAREfXFIICIyE/keoDU6DDoNL798+qPgWFyYTCLgomIqD8GAUREfuKPzkAyeSegxeh9i1C5JoBFwURE1B+DACIiP/FHZyBZbITvOwGNHBRGREQuMAggIvKTK45BYek+DAqTxTsKg32pCWAQQERErjAIICLykyplJ8D3IMAfNQGNck0ApwUTEVE/DAKIiPzAYrOjps1/6UBKdyAvgwCrza4MGmN3ICIi6o9BANEY8x97zuHhN4+hx2ob7aVcVWrbTLA7ZgT4cyeg3WSFxWb3+Pxmo1mZWZDEdCAiIuqHQQDRGNLU2YP/2FOGd4qrsOdM/Wgv56oiFwUDwLjYMJ+/X3xk79P7Vi86BMmDwgAggUEAERH1wyCAKIB2HKzAD18rQpsPbR796Wxth/L1vnMNo7iSq4/cHjQlWg+9Ru3z94uL6A0CWrwoDpaLgtUqAbHhWp/XQ0REVxfNaC+A6GrV3GXGr987A6tdRE5iJH58y5TRXhJKatqVr/eXNUAURQiCMIorunr4sz0oAMRG9N64e1McrMwIiNRBpeKfMRER9cWdABrglQMXsfHVo+jssY72UkLaByeqYXUkib/+5aWgyMF33gmobjPhfEPnKK7m6nLFj52BAECrVsEQJj2n8WYngNOCiYhoKAwCqI82owW/212Cj8/UYdex6tFeTkj7W3GV8nVjpxkfnKgZxdVIzta29/n/fecaR2klV5+qVikdyB8zAmRyXUBzlxc1AfKMALYHJSKiQTAIoD4+La2DzfH0+mRV2yivJnRdbOxC8aVWAEB2YiQA4K+FFaO4Iqll5Lk66cm//HR4XxnrAvzF3+lAQG9dgDc7AQ1yEBDJ9qBERDQQgwDq4+PTdcrXJ6taR3Eloe1dxy5AWkwYnrxzJgDg+JU2FF9qGbU1VTR1wWyVWk3et3gCAODQhaagSFMKdVabHTVtJgD+SwcCnHcCvKkJ4KAwIiJyjUEAKUwWG/Y6dYwpre3gDaIXRFHEu8ekIOCb16RjXlYcpqdFAxjd3YCSGqkeIEqvwd3zxgMATBY7jlaMXmBytahtNyk7aP4MApSdAG+CgA7HTgAHhRER0SAYBJDii/JGGM02yM1iLDYR52pZOOqpokstqGyS8sO/dU06BEHA2sVZAIAPTtagvsM0KuuS6wGmpBqQZNBjRroUmDAlyHdVTjMC0vxaEyB1CGr2oUUoC4OJiGgwDAJIIacCzc+KR0q0dONwgilBHvtbkbQLMD0tGrkpBgDAN2alIS5CC4tNxOuHL4/Kus46dgLyUqU1XTc5CQCLg/1BrgdIMugRpvV9RoBMnhrs6U6A3S4qKUQMAoiIaDAMAggAYLOL2FMiBQG3TE9FfnoMAOAUi4M90mO14X1HF6BvXZOuvB6mVWP1fCkFZ+fhSiU3fyTJ7UHzxkk7AMscQUBJTfuo7U5cLfzdHlQW70gH8nQnoK3borSnTWA6EBERDYJBAAGQUliaHE8Ob5mWgvz0WADAiSsMAjzxeWkD2rotUAnAN2an9XlvzcIJUAlAfUcPPjpdO6Lrauu2oKpVulGd6tgJmDshDhE66an1gTLuBvhCnhbsz85AABDrCAJaPWwRKqcCAUASdwKIiGgQDAIIAPCx46Z06rhoZMZHID9Delp8rq4DJguLg931jiMVaOnkJCQbwvq8lx4bjlumpQIY+QLhUqchYbmOIECnUWFRTgIAYD+DAJ8EbCfAm/JCJAAAIABJREFUkQ7U0WP1aPdIbg8qCL3fg4iIyBmDAIIoivj4jCMVaFoKAGCGIx3IYhP73ECSa21GCz49Ww8A+LZTKpAzuUD4q8qWEU21kouCM+LCER2mVV6/bnIiAGB/WQPsjvQR8py8y+LPQWFAb2EwALR6kBIktweNi9BBo+Y/80RENBB/OhBK6zqUbja3TpeeVCcbwpAaLT3JDpWhYYXljfjV/55Cffvo5Le/f7IaZpsdETo1bpmeMugxC3PiMcVRLPyXEdwNKFGKgqP7vL4sV6oLaOw0o6TfNGFyj80uoro1MDsBcotQwLO6gKZOtgclIqKhMQggpStQRlw4po4zKK/nZ0i7ASdDoC7AYrPjwTeP4dWDlfhewZdo6/Ysh9of5AFhK2akIkKnGfQY53ahu45XKzdrgSbvBDj/+QLSNGP56TW7BHmnrt2kFOH6uyYgJlyrtOz1ZGCYXBOQEMl6ACIiGhyDAMLHZ6R6gFumpUKQ7zgApUNQKOwE/ONMHRocw5HO1nZgw6tHR7SW4XKzEUccQ7e+5SIVSHbHNWmIDtPAbLXjjSOBbxdqt/emdPXfCRAEQdkN2M95AV654jQjwN87ARq1CjHhUkpQiwfFwY0dnBZMRERDYxAwxlW1duNUlfSUuH8Ki7wTEArFwf91qBIAMD5eehL75cVmPPTGMWWKa6C949gFSInWY/HExCGPjdBpcPe8TADAzkOVsNoC2y70cosRRrP055fXbycAAJY56gKOVrTAaLYGdC1Xo6pWKZUuMUrn1xkBMm/ahDYyHYiIiIbBIGCM+4ejK1BchBbXTojr8568E2C1i0qP+WB0vqETheebAAC//sZ0/PLrUwEAH52uxf/ddRqiGNhAQBRFJQj45ux0qFXCMGcA/7wwC4IAVLeZ8A9HUXagyPUAYVoVshIiB7y/eFIiVAJgttlx+EJzQNdyNbrS7CgK9nMqkMybgWGcFkxERMNhEDDGyV2BbpqaMqCLSGKUHuNigr84+LXDlwBIqRjLcpNw/3U52HBdNgBgx6FKPPtpeUCvf/xKGy42dgEYPhVINj4hAjflJQMIfIGwXA8wJcUwaIASE67F7ExpLsQ+pgR5LFDtQWVxEY50IC+6A3FGABERucIgYAxrNZpx+KL05FduDdqfUhdwpXXE1uUJk8WGt7+6AgD47vzxyk3uz26bijscw7qe+sc5vPHlpYCt4Z0i6fp5qQZMHRc9zNG95ALhwxebUVITuM48Z110BnIm1wXsO8cgwFNXWuVBYYEKAjzbCRBFsXcnwMB0ICIiGhyDgDHs07P1sNlFhGlVuG5y0qDH9BYHB2f7yPdP1KCt2wKtWsB3rs1UXlepBPzhrllKH/yfv3MSewKQdmOx2fHeiRoA7u8CyJZOSsSk5CgAgR0eJu8EDFYPIJP//M83dCk978k9VfJOgJ9nBMjkYV/NRvcKgzt7rOhxDBZjdyAiInKFQcAYJrcGXTY5CeG6wQsaR6I4WBRFrwdV7TwsFQTfOj0VSf06oeg0KrywZi7y02NgF4EfvlaEryr9m/O+t7QBzV1mCIJUD+AJQRCwdtEEAMC7x6o8Ggblrq4eKyqbpSfVQ+0EzMqIQXSY1NZ0P3cD3Ga3i0rQ5O/2oDJPawLkVCCA3YGIiMg1BgFjlMliw17Hzd4tjgFhg5F3Amx2MSApK1Wt3Zj320+wavtB9Fg9CzJOV7eh+JKUprRm4YRBj4nSa1Bw3zxMSIhAj9WO9X85ivJ6/xU5v3NMKgheMjERqY76CU98e04GDHoNTBa7ktbkT+fqOiDXReelut4J0KhVWDJJnh7MeQHuqu/ogcUmzwgI0E6A3B3I7SCgd/ZEQiTTgYiIaHAMAlzYvXs37r33XsyZMwezZ8/G7bffjueffx4m0+hMo/W3A2WN6LbYoBKgFKgOJiFKrwyTCkRx8Et7z6OxswdfVbbgmU/KPDp3p6MgeFJyFBZkx7s8Lsmgx6vr5yMxSoe2bgu+98qXqGnzPeWl3WRROvvc4WEqkCxSr8E/OWoXPg5AupLc1Sk1Okx5ouyKXBdwoLxxxFqrhrorLUbl6/RA1QTIOwFu7hQ1OuZlGMI0AWlZSkREVwcGAYN48skn8fDDD+Po0aMwm80QBAFlZWXYtm0bVq1ahdbW4CyS9YQ8IGx+dvywN4cz0qU0En9PDm7uMuPNo73Dsl7ce8Hta3T2WPG/jrac9y4Y32fI2WAmJETiP++bj0idGtVtJmx997T3C3f48GQNzFY7wrQqrJjhejdlODdOkYKwrypb/D7p+GzN8PUAMrl+oq3bghNBWggebOTOQAmROpdTon0VHyl1BzKabW6l5DV2sTMQERENj0FAP7t27UJBQQHUajW2bt2KoqIiFBcX45VXXkFSUhLOnTuHRx99dLSX6RObXcSeknoA0pTg4QRqcvCOg5UwWewwhGkwKTkKNruIn7x9HGbr8MOz3imuQpfZhjCtCt+ek+HW9fIzYvC7b+cDAPaU1PmcFvS3IikIuXV6KqL03t8ALp6UAJ1aBZtdxBfl/k3FKXExKXgwGXERyEmS5gjsOze2U4Jq20x4p/gKPj1bN2SamlwPEKhdAKC3OxDg3m6AvBOQwEFhREQ0BAYBTmw2G5599lkAwIYNG7BmzRrodNIP0qVLl+KFF16AIAj47LPPUFRUNJpL9clXlS1KfnH/KcGDyc+QesiX1Xf6rTjYZLHh1YMVAKR8/n+/ayZUgpS+8vznQ/f1F0UROx0Tgr8xKw0x4Vq3r3v7zDRMSJAKOP+876JXawekAWVye1VPuwL1F6HTYEGOlM70eWm9T9/LmSiKyk7AVDd2AgCpSBwA9o+xeQFmqx0Hzzfh9x+WYMV/7MPC33+Ch988jvV/OYp5j+/Bv759AoWDpEnJ6UCBqgcA+gUBXcPvFHFQGBERuYNBgJPCwkJUVlZCpVJh7dq1A97Pz8/H0qVLAQDvvvvuSC/Pbz52TAmenhbtVkcT5+LgM34qDn77qyto6jJDp1Zh3eIsXDM+DvdflwMAePbT8iGLkIsutSi57vcuGLwg2BW1SsD9S6VBYu8UV6G+3bsaj217pPqF8fERWOooqPXF9Y58/L3nGvw24bimzYR2kxWAezsBALAsV/q1FF9uRbvJv6lJwaa6tRuvHb6Eja8exZx/+we+++dD2L73gvLZitSpoRKAdpMVbx69jHtePoxFv/8Ev3nvDI5dboUoik6DwgLTGQgAosO1kGe8ubUTwCCAiIjcwCDAyeHDhwEAeXl5iI8fvNB08eLFAIADBw6M2Lr8SRRFpQDVnVQgQOpTrhQH+6EuwGYX8ef9FwBIT9GTo6WuOo/cnIvsxEhYHWlBVtvgaUE7D0kFwfnpMZjlmHTribvmZiI+Ugezze7VtN7S2g68d6IaAPAvN00eMGnZG8sddQF17T0oqfFP9yJ5PoBWLShpPsNZkJ0ArVqAzS6isLzJL+sIJrVtJjz/eTlufXofFj/xKX7+zkl8fKYOnT1SsDRtXDR+sHwi/nvTIhx77BYc+vlNeOyfpikTles7elDwxUXc8dwXWP7Hz3HsslQ7EcidALVKQKwHHYLkFqEMAoiIaCiBqWQLUeXlUhpKTk6Oy2OysrIAAFVVVTAajYiICNwTwEAorevAJUffeHdSgWT56TGoau32S13Ax6drUdkkrWHDsmzl9TCtGn+4aya+s/0gTlW1Y/u+C/jhDZP6nNvSZcb7J6XhXPcuGO/V9cN1avzzwgnY9kkZ/utQJX5wwySPcvq3fXIOogjkJEYqU4l9NTEpEhlx4bjS0o3PSusxLc39ycOuyMHEpGQDtG4GKpF6Da6dEI+DF5qwr6zBp4LnYGGy2PCPM3V4+6sr2F/WAOeMnugwDa7LTcLy3CRcn5ukBKSyZEMY1i3Jxrol2bjUZMR7J6rxv8eqcK6uU/kMA1CC5ECJi9Ciucvs2U4ApwUTEdEQuBPgpL5eysdOSXF9c5yc3NtOs6Eh9PKm5QFhmfHhQ/aN708eGubrToAoiti+T9oF+NrUZExK7ruGeVnxWLsoC4CUclNW1/ep+NtfXYHZaodBr8E3fLgB/96iCQjTqqRUjyOXhz/B4XR1G3aflNKpHvyaf3YBAGlw2A2O3YC9pf75XMlpLVM9+HMGgOscKUH7/JiaNNJEUcTxy63Y+u4pLPjdJ9jyejH2npMCgPhIHdYtycLbDyxC0dab8dw9c7Dq2swBAUB/4xMi8MMbJuHjh6/HRw9dh83LJ2J8fATyUg1YkJMQ0F+PMjXYjZ2AJsdOAKcFExHRULgT4KSrqwsAEB7u+qme83udnZ0BX5M/NXT0YLfjKfot01KHbavpTK4LKKvvQLfZ5nLC8HCOVLQoKRSbrp846DE/XTEFn5ytw+Xmbvzk7RP4n82LoVYJsNtFvPallAr07TnpPrVkTIjSY9XcTOw4VImCAxfxvUUT3Hpa/h+OWoDJyVG4faZ/dgFky6ckYcehSnx1SWoV6knB82A8aQ/qbNnkJPzho1JcaelGRZMR2YnupRKNNlEUcbGxC5+U1OOtry7jXF3v30+1SsANU5Jw19xM3JiXDJ3Gt+AtLzUaeSui8a8r8nxdtlvk4uDhpgabLDYltSmJOwFERDQEBgFOrFbph6dW6/rmS+4WBEjdhLxhs9lQWlrq0TlGo5R64M55oiiivsuK8009KGvqQbnjv+bu3vVONZg9WkO4STrXLgIfHjqJ6SnepT88tUfKpZ+apIfBVI9SF0+9fzQ/Dv/6UTeOXW7FE+8cxl0z4lBUbcTFRilQW5xi9/j3sL8b04GdgtTm8eW/F+HGiUPfLJ9rNCnDwe6eHoXysnM+Xb+/RJsdWhVgsYt4a98JXJcV5fX3MlvtON8g3QRHWds9+r1SiyJiwtRoM9nwzhen8fW8GK/X4S5PPt/OajssOFbTjeM1Rpyo7Uajse/fyQmxOtwy2YCbJhoQF64B0IaL5/0/9C7QVFapALmytmnI36MjV7qUrzsbqlFq8u9upc1mg1rNAWRERFcDBgFOwsKkdACLxXVXFLO590ncUMHCSDPbRLx1sgUn67pR3tSDjp7Bi2rVArAwMxLTkodOfegvOkyNlCgN6jqtKG/q8SoIqGw14/Bl6WbvrhlxQ+5EzB4Xga9PicYHpe34a1EzFmZG4oOz0s3bjJQwZMX5nuqQFq3FkgmR2F/RhbdOteCGnKgh1/RqkdQSNDtOhyUT/P90PEyrQn5qOIqqu3HkSpdPQcClNouS+54d59kTYZUgIDtOh2M13ajrsnq9hkCo77TgeG03jtd040RtN+o6B64vRq/CsmwDbplswOQEvUc7XsEqWi/tXLT1uH7wYLOLeOmIVMw9MzUMKVH8552IiFzjTwknkZHSjZ3J5LptZHd3t/J1VJR3N2lqtRpTpkzx6Bz56Z+r894pvoJXi8/3eU2nUWHquGhMT4vGjLQYzEiPRm6KAWFa757kzcnqxIenalFnCfN4/QDwytvHAQDZiZG47+Y5UKuGvjl7Mmsijj29D1Wt3Xj6cCtKahzFxDfkYcoU33rzy34ckYL9z32BC81mNKgTcJ2jT35/X1W24EiVVDj+s9vzMTUvMAWzK+u1KKouQXGtGbm5uV7fwJ786gqAy0iI1GHh7Gkef5+sYiOO1XTDro306s/aU8N9vmvauvHTt09gf9nAIWbRYRosyEnAopwELJqYgCkpBqiG+WyFmkl1GuBkK3pErcvfox0HK3Cp1QxBAH636lrkpft/B+fYsWN+/55ERDQ6GAQ4SU1NxfHjx5UC4cHU1UnpIIIgIDHR9/7w/nLskpRnPyk5Cg9cPxEz0qMxMSnK7a4w7sjPiMGHp2pxsqrV43Pr2k14p1iasHv/ddnDBgAAEKXX4Hffzsfagi9xqkrKb0+I1Pm1Y83szFjMz47Hlxeb8dK+Cy6DgKf/IaX+5KfH4OZp7ndV8tTyKcl4/IMS1Hf04ExNO6aneXcj51wP4E0gkWSQdloaHJ1mRtNHp2rx6N9OoNUo7dAZ9BrMz47HookJWJiTgKnjot36PIUyuSag1UV3oDajBX9yfEa/MzcTMwIQABAR0dWFQYCTSZMm4e9//zsqKipcHlNZKU2qTU9PH7KAeKSdqpZu+m6eloK75mYE5BpycXB5fSeMZqtHhbn/+UUFLDYRCZE63DnH/fVdn5uE71ybgf8+egUAsOraTOg1/s1J3rQsB19ebMb+skacrm4bcON9+EITDpRLT6Afudn7p/PumJgUicz4cFxu7sbnpQ3eBwGOzkDuDgnrTwkCOkYvCOg22/BvH5zBa4elYvC4CC1+96183DwtxW9dmUKF0h3IRRDwzKdlaDFaEKlT48e35o7k0oiIKESNrZ+kw5g/fz4A4MyZM2hvH3xibWFhYZ9jg4HNLioTdqf7ob+8K3IQYBeBM9XuTw7uMFmw87AUPN23OMvjdKRffH0ashMjER2mwZqF3s0GGMoNU5IxKVlK7fqzo32pTBRFPOV4wjo7MxbLpwy+U+AvgiBgea7vrULlQWGetIF1NtpBwOnqNtz+//YrAcCSSQn46KFluC1/3JgLAAAgzhEEmCx2dJv71gVcaOjEXx1D73544yQkGzyr9yEiorFp7P00HcK8efOQlJQEq9WKgoKCAe+fOnVKmRS8evXqkV6eSxcbu2B03BjM8PLJsTtiI3TIjHdMDvZgaNgbX15Gh8mKcK0aaxZO8Pi6MeFa7P6X61D4s5uQEef/4WwqlYCN10kD4t47UYOq1t66j8LzTfjyolQQ/ONbArsLIJMDDblVqKcaOnqUqbFTx3m5ExAl3Ug2dZlhs4/crAC7XcTL+y/gW88V4nxDFzQqAT+7LQ871i9AyjB9/K9m8RG9xd39dwN+t7sEVruIjLhwrF+S3f9UIiKiQTEIcKJWq/HQQw8BALZv346XXnoJPT3Sk9CDBw9i8+bNEEURy5cvx6xZs0ZzqX2crpZuyA16DcbHB3aC8cz0WADuDw2z2Owo+OIiAODueZnKE01PhevUHk319dQ3r0lDskEPm11EwQFpvaIo4qmPpYLV+VnxWDppZGpAFk1MgE6tgs0u4sAghbDDkXcBVAKUHQ5PyTsBNrvo1pRaf2g2WnHfX47g8Q9KYLbZkZ0Yib/9YDE2XT/xqiv09ZTz3xvnWQH7yxqwp0SqYfr5yqleF/0TEdHYwyCgnzvvvBOrVq2C3W7HU089hblz5+Kaa67Bfffdh/r6euTm5uKPf/zjaC+zj9OO1JypadEBv1mSCw7d3Ql473g1atpMUKsEfH9p8D6l1GvUWOd4ivrGl5fQ1m3B3nMNKHIUXD8c4FoAZxE6DRbkxAMAPi91XaTuytkaqR4gJynK65tCOQgARiYl6MiVLmz+38vYd05KgfrOtRl4f8tSzMyIDfi1Q0F0mEYpfpanBlttdjz+fgkAKUi9zY8F80REdPVjENCPIAh4/PHH8fTTT2P+/PkICwuDxWJBVlYWHnjgAbz++uswGLzLsw6UU44b8kCmAslmZjiKgxs60dUzdA95URTxkiPHfmX+OGQGeJfCV/csGI9InRpdZhv+61Cl0m1l8USp9eRIWj7FURdwrgGi6Fk6TomP9QAAEBuuhcZx0xnIIMBqs+OJD8/il/+oQavJBkOYBs/ecw3+cNcsRAZw5yfUCIKAuAhpLom8M/PGkcsoreuAIABbb/e8DSwREY1t/CnrwsqVK7Fy5crRXsawRFFUdgJmpAeuKFgmBxqiCJypace8rPhBj7PbRWzfd0HpUrNpWU7A1+armHAtvjt/PF4+cBHbPimD2SoNXHvk5pHvtrJ8ShL+7X141SpU3gnwth4AkOokEqP0qG03BSwIqGs3YctrxfiyQqq5mJ4chu3rFgWk7uNqEBehQ2OnGc1dZrR197YEvWtOBvIz2BKUiIg8w52AEHelpVspHvW2naQnYiK0St2Bq7qA8voOrNp+EE9+dBaAdEMbKn3L1y/NhkYlKAHAstwkXOsi0AmknMRIpQj7cw+6BFlsdpTXdwLwbScACOysgC/KG/H1Z/YrAcCqGbH4w23pDACGINcFtBgtePbTMjR3mRGhU+MntwZ+mBsREV19GASEOLkoWK9RYWJS5IhcU37q2L8uwGKz47nPyrFy2wF8VdkCAFg9LxPPfPeaEVmXP6TFhuOfZqUp/z8auwBA31ahntQFXGzsgtkmBTB5PuwEAIFpE2q3i3jmkzKseeUwGjvNiA7T4M/fuxb3z0tU0o9ocHKHoOJLLfiL3BL0hklIHsNdk4iIyHtMBwpxSlHwuOgR65+enx6DD07U9AkCTlW14advn8AZx7yCzPhwPPHtmVgyQh11/OnBmybj+JVWLJuchNmZo1eYunxKEnYcqkTRpVa0dVsQE64d9hx5XoQhTIO0GN9uDpOi/BsENHX24OH/Pq4U/+anx+D5e+cgMz4CpaWeT6Eea+SdgP2OjlHpseFBXWxPRETBjUFAiJOLggM5JKy/mY7UnvMNnWjq7MHLBy7ipX0XYLOLEARg3eJs/J9bcz2aKBxMshIj8emPl4/2MqRWoRoVzFY7DpQ14uszxw17jlyDMTU12udCUX/uBHxV2Ywf7ixGbbsJAPDPCyfgl7dP9fv056tZfGTfIPBnK/PYEpSIiLwWmndppDilFAWPXM799PTe4uBbnt6HJkfLwsnJUXjyrpmYMz5uxNZyNYvQabAgOx77yxrxeWm9e0GAYycgb5zvHaz8VRPw8v4LeOLDs7DaRUTo1Hjizpn4hlPKFbknzmlg2LUT4vD1/OE/D0RERK4wCAhh9U6dW0ZyJyAmXIushAhUNBnR1GWGRiXgB8sn4oc3TuKTXT9bPiVZCgLONcBuF4edAyHvBOSl+v558MdOwJcXm/H4B1Iv+ykpBjx37xyvB5iNdYlRvbMbfvVPbAlKRES+YWFwCJPrATQqAbkpIzu7YOlkKdc/Pz0G721ZikdumcIAIACWT0kCIN2Iy/UWrtR3mFDTJqXb+HMnoK3bgh6rzavvceKKlOufGR+Od3+4hAGAD26eloKbp6XgV7dP4xA1IiLyGXcCQpjcGWhyimHEc4O33j4Nq+ZmYnrayBUkj0Vyq9DLzd3Ye65h0LSvzh4r/lpYgT/vlwazqQTpqbuvkpyePDd2mpEeG+7x95CDkolJUQjXMUj0RaRe6qRERETkD7x7C2GnqqQnwyOZCiTTa9SYlRnLACDAhmoV2tljxXOflWPpk5/i3/9eilajBeFaNR69Lc8v03blnQDA+5SgmrZuAMA4HzsVERERkX9xJyCEnXLsBMwYhSCARs4NeU6tQo0WaNQCXj1YiZf2nUeLURoUF6ZV4XuLsrBxWU6f3HFfROo1iNCpYTTbfAgCpJ2AcTGe7yIQERFR4DAICFFtRguutEhPWUNlGi95Z1FOotIq9OfvnsTB801odnRkCtOq8M8LJ2Djsol9ntz7S5JBj8omo/dBQKscBHAngIiIKJgwCAhRcj2AIEiDwujqFa5TK61CPzhRA0CaEL1m4QRsuj4HyYbA3WAnRXkfBFhtdtR3cCeAiIgoGDEICFFyKlB2YqRf8r8puH1jVhr2lzVCp1Hh3gXjsfn6iUiODvzT9d5ZASaPz63v6IFdlL4eF8udACIiomDCu8cQJbcHnZHGVKCx4K65GchOjMT4hIiAPvnvz5dZAXJRMMB0ICIiomDDICBEnaqSdgJGozMQjTxBEHBtVvyIX1cuMvYmCKh21APEhGsRoeM/NURERMGE/R1DUFePFRcauwCwKJgCqzcdyPMgoLaNRcFERETBikFACDpb2w7RkWvNnQAKpCSnnQBR/tC5qZozAoiIiIIWg4AQJA8JS48NR2yEbpRXQ1czeSfAZLGjs8fq0blKe1AvJg0TERFRYDEICEFye9AZ6dwFoMDyZWpwTbsUBKRxJ4CIiCjoMAgIQfJOwHR2BqIAS4jq3WnyOAholdKBUjkjgIiIKOgwCAgxPVYbztV1AOBOAAWeXqNGbIQWgGfFwWarXTmeOwFERETBh0FAiCmr64TVMYGJMwJoJCR50Sa0vsOkFK+zJoCIiCj4MAgIMfJ8gMQo/YhMjCXyZmBYTVvvhOFUfk6JiIiCDoOAEHOKRcE0wrwJAqod9QBxEVqE69QBWRcRERF5j0FAiDldLRUFMxWIRoqSDuRBTUDvoDCmAhEREQUjBgEhxGYXUVIjdwbiTgCNDF/SgTgojIiIKDgxCAghV9otMFnsAIAZ6dwJoJHhXRDgmBYcyyCAiIgoGDEICCHlTdJNWHSYBhlxTLOgkSEHAU1dZtgcnamGU8N0ICIioqDGICCElDdJN1bT02IgCMIor4bGCjkIsNlFtBjNbp1T3cp0ICIiomDGICCElDdJN2DsDEQjSS4MBtxLCTJb7Wh0FBFzJ4CIiCg4MQgIEXZRVNKBprMzEI2guAgd1Cpp58mdIKCuvXdGQBprAoiIiIISg4AQUdthhVEpCuZOAI0clUpAYpQOgHtBgDwjAABSOCiMiIgoKDEICBHnm6Wbr3CtGtmJUaO8GhprlA5BbswKqHXsBCRE6hCm5aAwIiKiYMQgIESUOVKBpo4zKKkZRCNFGRjm1k6AoyiYqUBERERBi0FAiJDrATgfgEaDJ7MC5BkBqdEsCiYiIgpWDAJCgOhUFDyDRcE0CjwLAqSdABYFExERBS8GASGgrr0HbSYbAGBaGouCaeQp6UBu1AQo04LZHpSIiChoMQgIAaer2wAAGhWQm2IY5dXQWJRkkJ7qu7UTwEFhREREQY9BQAg4VdUOAMiK1UOn4R8ZjTw5Hait24Ieq83lcSaLDU1d0lA7BgFERETBi3eUISA+UgsAmJ8ZMcorobFKDgIAoLHT7PI450FhTAciIiIKXprRXgANb83CCRin7kCaQTvaS6Exqk8Q0NGD9NjBb/Dl9qAAkBKjH/QYIiIiGn3cCQgotR/CAAASWUlEQVQBgiAgM0bH+QA0aiJ1aoQ7Bn8NVRdQ2y4VBSdG6aHXcFAYERFRsGIQQETDEgTBranB1SwKJiIiCgkMAojILe7MCuhtD8oggIiIKJgxCCAityizAoZKB1IGhbEomIiIKJgxCCAit7izEyCnA6VyJ4CIiCioMQggIre4UxNQ286aACIiolDAIICI3DLcToDJYkOzY1AY04GIiIiCG4MAInKLc02AKIoD3q9p650RkBrNnQAiIqJgxiCAiNwi7wR0W2zoMtsGvC93BhIE1gQQEREFOwYBROQW56nBg6UE1TiKgpOi9NCq+U8LERFRMONPaiJyS0KUTvl60CCAMwKIiIhCBoMAInKLXqNGbIQWgKsgQO4MxKJgIiKiYMcggIjc1lscbBrwnhIExHIngIiIKNgxCCAitw01K6C6lelAREREoYJBABG5bahZAb2DwpgOREREFOwYBBCR25xnBTjrNtvQarQAANKYDkRERBT0GAQQkdtcpQNVOzoDAUAqdwKIiIiCHoMAInKbq3SgWkdRsEoAUpzmCRAREVFwYhBARG6Tg4DGTjPsdlF5XS4KTjaEQcNBYUREREGPP62JyG1yEGCzi2gxmpXX5fagqewMREREFBIYBBCR2+TCYKBvXYAcBLAomIiIKDQwCCAit8VF6KBWCQD61gXUOAqDU6NZFExERBQKGAQQkdtUKgGJUToA/YKAVu4EEBERhRIGAUTkkcE6BMk7ARwURkREFBoYBBCRR/oPDOvqsaLdZAXAwmAiIqJQwSCAiDzSf2BYjdOgMKYDERERhQYGAUTkkf7pQHJnILVKQLKBQQAREVEoYBBARB7pnw4kFwUnG/RK5yAiIiIKbgwCiMgjSY6n/b3pQFIQMI71AERERCGDQQAReUROB2o1WtBjtfV2BoplZyAiIqJQwSCAiDwiBwEA0NRpRrW8ExDNnQAiIqJQwSCAiDziHAQ0dPSgljsBREREIYdBABF5JFKnRrhWDUAKApRpwawJICIiChkMAojII4IgKLsBFxu70NHDQWFEREShhkEAEXlMDgJOVLUpr6UxHYiIiChkMAggIo/JswJOXGkFAGhUAhKj9EOdQkREREGEQQAReUzeCahsMgIAUqLDOCiMiIgohDAIICKPOXcIAjgojIiIKNQwCCAijw0IAlgPQEREFFIYBBCRx/rn/3MngIiIKLQwCCAijzEdiIiIKLQxCCAijw0MApgOREREFEo0I3GRnp4evPnmm/jwww9RVlYGk8mEuLg4zJkzB2vXrsWcOXNcnltfX4/nn38e+/btQ319PWJiYjB79mysX78ec+fOHfK6u3fvxs6dO1FSUgK73Y6MjAysXLkS69evR1iY6yeXvlyTaCxIjNL1+X/uBBAREYUWQRRFMZAXaG5uxn333YfS0lIAgFarhVarhdEotRYUBAGPPPIINm7cOODcy5cvY/Xq1WhsbAQAGAwGdHV1wW63Q6VSYevWrbjnnnsGve6TTz6JgoKCQa+Zm5uLHTt2IDY21q/XdMexY8cAALNnz/boPPn3b8qUKV5fm8ifZv36Y7R1WwAAX/7iJiQbvA8E+PkODd7++0VERMEn4OlAP/7xj1FaWorY2Fhs27YNxcXFKC4uxp49e3DrrbdCFEU89dRT+PTTT/ucZ7VasXHjRjQ2NmLWrFn46KOPcPToURQWFuLuu++G3W7H448/juPHjw+45q5du1BQUAC1Wo2tW7eiqKgIxcXFeOWVV5CUlIRz587h0UcfHXCeL9ckGmvklCCtWkBiJAeFERERhZKABgHFxcUoLCwEAPz+97/HihUroNVqAQCZmZnYtm0bFixYAAB47rnn+pz73nvv4cKFCzAYDNi+fTuys7MBAHFxcfjNb36DG2+8ETabDdu2betzns1mw7PPPgsA2LBhA9asWQOdTkpdWLp0KV544QUIgoDPPvsMRUVFfrkm0VgkTw1OiQ6DioPCiIiIQkpAg4C9e/cCACZMmIAbb7xxwPuCIGDVqlUAgNOnTyvpOgDwxhtvAADuuOMOxMXFDTj3gQceAAAUFhaivr5eeb2wsBCVlZVQqVRYu3btgPPy8/OxdOlSAMC7777b5z1vr0k0Fsk7AWksCiYiIgo5AQ0CsrKycNttt+Hmm292eUxSUhIAQBRFdHV1AQCMRiNOnDgBAFi0aNGg5+Xn58NgMEAURRw4cEB5/fDhwwCAvLw8xMfHD3ru4sWLAaDPeb5ck2gsmpEeDQDIz4gZ5ZUQERGRpwLaHeiOO+7AHXfcMeQxckqOTqdTnr5fvHgRdrsdAJCTkzPoeSqVCuPHj8fp06dRVlamvF5eXj7keYAUnABAVVUVjEYjIiIifLom0Vi0fkk25k6IV4IBIiIiCh2jOiego6MDO3fuBAAsWbIEGo0Ukzin2qSmpro8Pzk5GQDQ0NCgvCafm5KSMux5zuf6ck2isUijVmHuhDjoNerRXgoRERF5yK2dgPb2drS3t7v9TfV6vZLm44rdbsejjz6KxsZGCIKAzZs3K+91dnYqXw/Vz19+z/l4OaUoPNx1nrLze/K5vlyTiIiIiCiUuBUE/PWvf1U67rhj/vz52LFjh8v3RVHEY489hj179gAANm3ahFmzZinv22w2AFJ/f0Fw3XVE7vojHw9IbT7lc4c7z/lcX67pKZvNpvRFd5dcNO3peUShgJ/v0GCz2aBWc+eHiOhqMCITg51ZrVb87Gc/w65duwAAK1aswIMPPtjnGL1e6jpisVggiqLLm3Kz2Qyg7w2//KTeYrG4XIN8nvO5vlyTiIiIiCiUuBUEbNmyBVu2bPH5Yp2dnXjwwQeVzjq33nornnrqKahUfUsTIiMjla97enpcpueYTCYAQFRU1IBz5fcG093drXwtn+vLNT2lVqs9nozKiap0NePnOzTIE4OJiCj0jVhhcF1dHe655x4lALjrrrvw9NNPK8XAzsaNG6d8PVQ//rq6OgB9C33lol53zhMEAYmJiT5fk4iIiIgolIxIEFBRUYHVq1crT/s2b96M3/72ty5zS7OyspTg4OLFi4MeY7fbcenSJQDApEmTlNflrysqKlyup7KyEgCQnp6uFAn7ck0iIiIiolAS8JqA2tparF+/HtXV1VCpVPjVr36F7373u0Oeo9Vqcc011+DIkSM4dOgQrr/++gHHnDx5UunQc+211yqvz58/H8899xzOnDmD9vZ2REcP7GFeWFioHOuPa3pCLij2dFvd2/OIQgE/36HBl4YIREQUXAK6E2Cz2fDQQw+hqqoKKpUKf/jDH4YNAGQrV64EAPzP//wPmpubB7z/4osvAgCWLVuGjIwM5fV58+YhKSkJVqsVBQUFA847deqUkpK0evVqv1xzJKjVanbloKsWP99EREQjSxBFUQzUN3/jjTfw2GOPAQB+9KMfeVRcbDabcfvtt6OyshJTp07FE088gby8PLS1teGpp57Cm2++CY1Gg507d2L27Nl9zn377bfxi1/8AiqVCg8//DDWrl0LvV6PgwcP4qc//Snq6+uxfPlybN++3W/XJCIiIiIKFQENAlasWKHk1yckJAzZfx+Qbt6dC3TPnj2LtWvXorW1FQBgMBjQ1dUFu90OAHj88cexatWqAd9HFEVs3boVb731FgAp1Uer1Sq9yHNzc/Haa6/BYDAMONfbaxIRERERhYqABQEtLS1YuHChR+d88sknA9Js6uvr8eKLL+Lzzz9HfX09wsPDMXv2bHz/+98f9vvv3r0br7/+OkpKSmAymZCeno4VK1Zgw4YNQ7b49OWaRERERETBLqA7AUREREREFHxGbE4AEREREREFBwYBRERERERjDIMAIiIiIqIxhkEAEREREdEYwyCAiIiIiGiMYRBARERERDTGMAggIiIiIhpjGAQQEREREY0xmtFeALm2e/du7Ny5EyUlJbDb7cjIyMDKlSuxfv16hIWFjfbyiIbV0dGBHTt2YM+ePaioqIDZbEZycjIWLFiAdevWITc3d9DzLl68iBdeeAEHDx5ES0sL4uLisHDhQmzcuBGTJ08e4V8FERHR1YcTg4PUk08+iYKCAgCAVquFVquF0WgEAOTm5mLHjh2IjY0dzSUSDamiogLr169HVVUVAECv10OlUqG7uxuA9Ln+7W9/i29+85t9zjtx4gTWrl0Lo9EIQRAQFRWFjo4O5Xv86U9/wte+9rWR/cUQERFdZZgOFIR27dqFgoICqNVqbN26FUVFRSguLsYrr7yCpKQknDt3Do8++uhoL5PIJavVih/84AeoqqpCRkYGCgoKcOzYMRQXF2PXrl2YP38+LBYLfvGLX+D06dPKeW1tbdi0aROMRiNuuOEG7N27F0ePHsW+fftw4403oqenBz/5yU+UwIKIiIi8wyAgyNhsNjz77LMAgA0bNmDNmjXQ6XQAgKVLl+KFF16AIAj47LPPUFRUNJpLJXLpo48+wvnz56FWq/Hss89iyZIlUKlUEAQBU6ZMwcsvv4ycnBxYLBZs375dOW/Hjh1obm5GZmYmnnnmGaSkpAAAUlJS8Mwzz2D69OkwGo148cUXR+uXRkREdFVgEBBkCgsLUVlZCZVKhbVr1w54Pz8/H0uXLgUAvPvuuyO9PCK37N27FwCwYMECTJ06dcD7er1eSQM6cuQIAEAURbzxxhsA0Cf4lWm1Wtx///0ApHoZs9kcsPUTERFd7RgEBJnDhw8DAPLy8hAfHz/oMYsXLwYAHDhwYMTWReSJadOm4dZbb8WyZctcHpOUlAQA6OzsBABcuHABDQ0NAICFCxcOes6iRYuUc4qLi/25ZCIiojGF3YGCTHl5OQAgJyfH5TFZWVkAgKqqKhiNRkRERIzE0ojctm7dOqxbt27IY+R0ttTUVADA+fPnAQCCICA7O3vQc+Li4hAbG4vW1laUl5djwYIFflw1ERHR2MGdgCBTX18PAEou9GCSk5OVr+Unp0Sh5PLly3j//fcBANdffz2A3s9+XFwc9Hq9y3Plz798PBEREXmOQUCQ6erqAgCEh4e7PMb5PTmVgihU9PT04JFHHoHJZIJer8f69esB9H6Wh/rsA1BmZMh/V4iIiMhzDAKCjNVqBSAVQbriXDBps9kCviYifzGbzdiyZQtOnDgBAPjlL3+JtLQ0AO599oHez798PBEREXmOQUCQkZ9yWiwWl8c4d0UZ7oaJKFh0dXVh06ZNSuegdevW4Tvf+Y7yvjuffaD388/PPhERkfdYGBxkIiMjAQAmk8nlMfLEVQCIiooK+JqIfFVfX49NmzbhzJkzAIC1a9cOGHjnzmff+X1+9omIiLzHnYAgI3dKGarosa6uDoDURSUxMXFE1kXkrfLyctx9991KALBlyxb8/Oc/H3DcuHHjAAAtLS1D7gbIn3/nAnkiIiLyDIOAIDNp0iQAQEVFhctjKisrAQDp6enDFlESjaZjx47hnnvuQXV1NdRqNX7961/jRz/60aDHTpw4EQBgt9tx6dKlQY9paWlBW1sbgN6/K0REROQ5BgFBZv78+QCAM2fOoL29fdBjCgsL+xxLFIzOnj2LjRs3oq2tDXq9Hs888wxWr17t8vjMzEykp6cDAA4dOjToMQcPHgQg1Q/MnDnT/4smIiIaIxgEBJl58+YhKSkJVqsVBQUFA94/deqUMil4qBsqotHU1dWFLVu2oK2tDWFhYXjppZfwta99bdjzbrvtNgDAq6++OqA2wGKx4OWXXwYAfOtb3xpylgARERENjUFAkFGr1XjooYcAANu3b8dLL72Enp4eANJT0M2bN0MURSxfvhyzZs0azaUSufTiiy8qKT2PPfYYFi5c6NZ53//+9xETE4OKigo88MADuHz5MgCpDuChhx7C6dOnERERgfvvvz9gayciIhoLBFEUxdFeBPUliiK2bt2Kt956C4DUClGr1cJoNAIAcnNz8dprr8FgMIzmMokGZTabsWjRInR2dkIQBCQkJAx7zhdffNHn682bNyvBb3R0NDo6OiCKIrRaLZ5//nksW7YsYOsnIiIaCxgEBLHdu3fj9ddfR0lJCUwmE9LT07FixQps2LCB7REpaJ06dQp33nmnR+eUlpb2+f+Kigq8+OKLKCwsRHNzMwwGA+bNm4cHHngA06ZN8+dyiYiIxiQGAUREREREYwxrAoiIiIiIxhgGAUREREREYwyDACIiIiKiMYZBABERERHRGMMggIiIiIhojGEQQEREREQ0xjAIICIiIiIaYxgEEBERERGNMQwCiIiIiIjGGAYBRERERERjDIMAIiIiIqIx5v8Ds1wI5P4xl/UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#分别使用线性回归，岭回归，Lasso回归进行数据预测\n",
    "lrg=LinearRegression()\n",
    "ridge=Ridge()\n",
    "lasso=Lasso()\n",
    "lrg.fit(X_train,y_train)\n",
    "ridge.fit(X_train,y_train)\n",
    "lasso.fit(X_train,y_train)\n",
    "\n",
    "#数据视图，此处获取各个算法的训练数据的coef_:系数，coef_可以理解为系数\n",
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "\n",
    "#线性回归 得到的coef\n",
    "axes=plt.subplot(221)\n",
    "axes.plot(lrg.coef_)\n",
    "axes.set_title('lrg_coef')\n",
    "\n",
    "#l岭回归 得到的coef\n",
    "axes=plt.subplot(222)\n",
    "axes.plot(ridge.coef_)\n",
    "axes.set_title('ridge_coef')\n",
    "\n",
    "#lasso回归 得到的coef\n",
    "axes=plt.subplot(223)\n",
    "axes.plot(lasso.coef_)\n",
    "axes.set_title('lasso_coef')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
